1. Data Overview

1. Data Overview#

The purpose of this notebook is to download, assess, and explore the data that will be worked with in this Neurocognitive psychology project. The dataset used is the Autism Brain Imaging Data Exchange I (ABIDE I) dataset. ABIDE I was developed as part of the 1000 Functional Connectomes Project, to further encourage discovery science on the brain connectome in Autism Spectrum Disorder. This dataset consists of MRI data of patients with and without Autism, from various different sites around the world. From this dataset we will be using the anatomical data, and the CSV file with all phenotypic information for the participants.

1.1. Loading the data#

Access to the ABIDE I dataset is granted through the NITRC website, a website where Neuroimaging Tools and Resources are shared. Here we can find the phenotpyic CSV file. Before downloading the actual brain images, it is important to get a general overview of what data and phenotypic information we are dealing with. To do this, we begin by downloading the .csv file, and reading it as a dataframe using pandas.

from os import chdir

chdir('/Users/gretivan/Desktop/Neuro_data')

import pandas as pd

Data_overview = pd.read_csv("Data_overview.csv")
Data_overview

	Unnamed: 0	Unnamed: 0.1	SUB_ID	X	subject	SITE_ID	FILE_ID	DX_GROUP	DSM_IV_TR	AGE_AT_SCAN	...	qc_notes_rater_1	qc_anat_rater_2	qc_anat_notes_rater_2	qc_func_rater_2	qc_func_notes_rater_2	qc_anat_rater_3	qc_anat_notes_rater_3	qc_func_rater_3	qc_func_notes_rater_3	SUB_IN_SMP
0	0	1	50002	1	50002	PITT	no_filename	1	1	16.77	...	NaN	OK	NaN	fail	ic-parietal-cerebellum	OK	NaN	fail	ERROR #24	1
1	1	2	50003	2	50003	PITT	Pitt_0050003	1	1	24.45	...	NaN	OK	NaN	OK	NaN	OK	NaN	OK	NaN	1
2	2	3	50004	3	50004	PITT	Pitt_0050004	1	1	19.09	...	NaN	OK	NaN	OK	NaN	OK	NaN	OK	NaN	1
3	3	4	50005	4	50005	PITT	Pitt_0050005	1	1	13.73	...	NaN	OK	NaN	maybe	ic-parietal-cerebellum	OK	NaN	OK	NaN	0
4	4	5	50006	5	50006	PITT	Pitt_0050006	1	1	13.37	...	NaN	OK	NaN	maybe	ic-parietal slight	OK	NaN	OK	NaN	1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1107	1107	1108	51583	1108	51583	SBL	SBL_0051583	1	2	35.00	...	NaN	OK	NaN	OK	ic-cerebellum-temporal_lobe	OK	NaN	OK	NaN	0
1108	1108	1109	51584	1109	51584	SBL	SBL_0051584	1	2	49.00	...	NaN	OK	NaN	maybe	vmpfc dropout	OK	NaN	OK	NaN	0
1109	1109	1110	51585	1110	51585	SBL	SBL_0051585	1	1	27.00	...	NaN	OK	NaN	maybe	ic-cerebellum-temporal_lobe	OK	NaN	OK	NaN	0
1110	1110	1111	51606	1111	51606	MAX_MUN	MaxMun_a_0051606	1	2	29.00	...	NaN	OK	NaN	maybe	ic-cerebellum	OK	NaN	OK	NaN	0
1111	1111	1112	51607	1112	51607	MAX_MUN	MaxMun_a_0051607	1	2	26.00	...	NaN	OK	NaN	maybe	ic-cerebellum	OK	NaN	OK	NaN	1

1112 rows × 106 columns

We can see here that there are 1112 subjects in this dataset, with 106 pieces of phenotypic information. At first glance, there appear to be various NaNs, though these all fall within columns which are not needed for the planed analysis.

Data_overview.shape

(1112, 106)

Data_overview.columns

Index(['Unnamed: 0', 'Unnamed: 0.1', 'SUB_ID', 'X', 'subject', 'SITE_ID',
       'FILE_ID', 'DX_GROUP', 'DSM_IV_TR', 'AGE_AT_SCAN',
       ...
       'qc_notes_rater_1', 'qc_anat_rater_2', 'qc_anat_notes_rater_2',
       'qc_func_rater_2', 'qc_func_notes_rater_2', 'qc_anat_rater_3',
       'qc_anat_notes_rater_3', 'qc_func_rater_3', 'qc_func_notes_rater_3',
       'SUB_IN_SMP'],
      dtype='object', length=106)

Here we can see what the different labels we are working with are. Important lables are SUB_ID, subject, FILE_ID, DX_GROUP, AGE_AT_SCAN and SEX.

1.2. Assessing the demographics#

We now want to look at the demographic information to establish wheter we are working with even groups. Since the goal of this data analysis is to see whether machine learning can predict whether an image is from a male or female participant, from an autistic or a control participant, or both, it is important to make sure the training sets are of equal sizes.

Data_overview.describe()

	Unnamed: 0	Unnamed: 0.1	SUB_ID	X	subject	DX_GROUP	DSM_IV_TR	AGE_AT_SCAN	SEX	HANDEDNESS_SCORES	...	func_fber	func_fwhm	func_dvars	func_outlier	func_quality	func_mean_fd	func_num_fd	func_perc_fd	func_gsr	SUB_IN_SMP
count	1112.000000	1112.000000	1112.000000	1112.000000	1112.000000	1112.000000	1112.000000	1112.000000	1112.000000	370.000000	...	1099.000000	1099.000000	1099.000000	1099.000000	1099.000000	1099.000000	1099.000000	1099.000000	1099.000000	1112.000000
mean	555.500000	556.500000	50752.684353	556.500000	50752.684353	1.515288	-646.819245	17.048864	1.147482	-102.560892	...	100.548498	2.116388	1.103531	0.005525	0.012778	0.131138	27.218380	12.952826	0.028416	0.686151
std	321.151055	321.151055	447.680666	321.151055	447.680666	0.499991	2461.828856	8.036419	0.354745	1273.177790	...	43.552970	0.345282	0.117803	0.006464	0.011212	0.157728	42.757583	18.216671	0.021070	0.464265
min	0.000000	1.000000	50002.000000	1.000000	50002.000000	1.000000	-9999.000000	6.470000	1.000000	-9999.000000	...	33.836513	1.581108	0.709671	0.000182	0.001693	0.016117	0.000000	0.000000	-0.015812	0.000000
25%	277.750000	278.750000	50354.750000	278.750000	50354.750000	1.000000	0.000000	11.658575	1.000000	48.000000	...	69.047209	1.862772	1.026977	0.001124	0.006333	0.050888	2.000000	0.995025	0.013075	0.000000
50%	555.500000	556.500000	50724.500000	556.500000	50724.500000	2.000000	0.000000	14.660000	1.000000	75.000000	...	87.681334	2.004123	1.080727	0.003441	0.010043	0.083163	9.000000	4.972376	0.031102	1.000000
75%	833.250000	834.250000	51153.250000	834.250000	51153.250000	2.000000	1.000000	20.085000	1.000000	91.000000	...	125.779269	2.328370	1.172068	0.006977	0.015189	0.143479	33.000000	16.977985	0.042821	1.000000
max	1111.000000	1112.000000	51607.000000	1112.000000	51607.000000	2.000000	4.000000	64.000000	2.000000	100.000000	...	326.544102	3.753481	1.571101	0.080158	0.098614	1.434912	288.000000	91.735537	0.104225	1.000000

8 rows × 87 columns

We can see here that the proportions of Autistic to control participants are relatively even. Since they are coded 1 (ASD) and 2 (Control) we would expect and even split to have a mean of around 1.5. Here we have a mean for the DX_GROUP of 1.51 so this is great. For the Sex group however, which is also coded 1 (Male) and 2 (Female), we are working with a very uneven split as seen in the mean of 1.15.

We can double check these individually as well to make sure that we are correct in this interpretation.

control = Data_overview.loc[Data_overview['DX_GROUP']==2]
control

	Unnamed: 0	Unnamed: 0.1	SUB_ID	X	subject	SITE_ID	FILE_ID	DX_GROUP	DSM_IV_TR	AGE_AT_SCAN	...	qc_notes_rater_1	qc_anat_rater_2	qc_anat_notes_rater_2	qc_func_rater_2	qc_func_notes_rater_2	qc_anat_rater_3	qc_anat_notes_rater_3	qc_func_rater_3	qc_func_notes_rater_3	SUB_IN_SMP
26	26	27	50030	27	50030	PITT	Pitt_0050030	2	0	25.12	...	NaN	OK	NaN	maybe	ic-parietal-cerebellum	OK	NaN	OK	NaN	1
27	27	28	50031	28	50031	PITT	Pitt_0050031	2	0	12.92	...	NaN	OK	NaN	maybe	ic-cerebellum_temporal_lobe	OK	NaN	OK	NaN	1
28	28	29	50032	29	50032	PITT	Pitt_0050032	2	0	19.80	...	NaN	OK	NaN	maybe	ic-cerebellum_temporal_lobe	OK	NaN	OK	NaN	1
29	29	30	50033	30	50033	PITT	Pitt_0050033	2	0	12.15	...	NaN	OK	NaN	maybe	ic-cerebellum_temporal_lobe	OK	NaN	OK	NaN	1
30	30	31	50034	31	50034	PITT	Pitt_0050034	2	0	14.77	...	NaN	OK	NaN	maybe	ic-cerebellum_temporal_lobe	OK	NaN	OK	NaN	1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1090	1090	1091	51566	1091	51566	SBL	SBL_0051566	2	0	41.00	...	NaN	OK	NaN	maybe	ic-cerebellum-temporal_lobe	OK	NaN	OK	NaN	0
1091	1091	1092	51567	1092	51567	SBL	SBL_0051567	2	0	34.00	...	NaN	OK	NaN	maybe	ic-cerebellum-temporal_lobe	OK	NaN	OK	NaN	0
1092	1092	1093	51568	1093	51568	SBL	SBL_0051568	2	0	37.00	...	NaN	OK	NaN	maybe	ic-cerebellum-temporal_lobe	OK	NaN	OK	NaN	0
1093	1093	1094	51569	1094	51569	SBL	SBL_0051569	2	0	36.00	...	NaN	OK	NaN	maybe	ic-cerebellum-temporal_lobe	OK	NaN	OK	NaN	0
1094	1094	1095	51570	1095	51570	SBL	SBL_0051570	2	0	42.00	...	NaN	OK	NaN	maybe	ic-cerebellum-temporal_lobe	OK	NaN	OK	NaN	0

573 rows × 106 columns

asd = Data_overview.loc[Data_overview['DX_GROUP']==1]
asd

	Unnamed: 0	Unnamed: 0.1	SUB_ID	X	subject	SITE_ID	FILE_ID	DX_GROUP	DSM_IV_TR	AGE_AT_SCAN	...	qc_notes_rater_1	qc_anat_rater_2	qc_anat_notes_rater_2	qc_func_rater_2	qc_func_notes_rater_2	qc_anat_rater_3	qc_anat_notes_rater_3	qc_func_rater_3	qc_func_notes_rater_3	SUB_IN_SMP
0	0	1	50002	1	50002	PITT	no_filename	1	1	16.77	...	NaN	OK	NaN	fail	ic-parietal-cerebellum	OK	NaN	fail	ERROR #24	1
1	1	2	50003	2	50003	PITT	Pitt_0050003	1	1	24.45	...	NaN	OK	NaN	OK	NaN	OK	NaN	OK	NaN	1
2	2	3	50004	3	50004	PITT	Pitt_0050004	1	1	19.09	...	NaN	OK	NaN	OK	NaN	OK	NaN	OK	NaN	1
3	3	4	50005	4	50005	PITT	Pitt_0050005	1	1	13.73	...	NaN	OK	NaN	maybe	ic-parietal-cerebellum	OK	NaN	OK	NaN	0
4	4	5	50006	5	50006	PITT	Pitt_0050006	1	1	13.37	...	NaN	OK	NaN	maybe	ic-parietal slight	OK	NaN	OK	NaN	1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1107	1107	1108	51583	1108	51583	SBL	SBL_0051583	1	2	35.00	...	NaN	OK	NaN	OK	ic-cerebellum-temporal_lobe	OK	NaN	OK	NaN	0
1108	1108	1109	51584	1109	51584	SBL	SBL_0051584	1	2	49.00	...	NaN	OK	NaN	maybe	vmpfc dropout	OK	NaN	OK	NaN	0
1109	1109	1110	51585	1110	51585	SBL	SBL_0051585	1	1	27.00	...	NaN	OK	NaN	maybe	ic-cerebellum-temporal_lobe	OK	NaN	OK	NaN	0
1110	1110	1111	51606	1111	51606	MAX_MUN	MaxMun_a_0051606	1	2	29.00	...	NaN	OK	NaN	maybe	ic-cerebellum	OK	NaN	OK	NaN	0
1111	1111	1112	51607	1112	51607	MAX_MUN	MaxMun_a_0051607	1	2	26.00	...	NaN	OK	NaN	maybe	ic-cerebellum	OK	NaN	OK	NaN	1

539 rows × 106 columns

As we can see here, we are correct in our above interpretation that the control and the ASD group are roughly even - with 573 control participants and 539 autistic participants.

We can do the same thing to check whether the male and female groups are of even sizes.

female = Data_overview.loc[Data_overview['SEX']==2]
female

	Unnamed: 0	Unnamed: 0.1	SUB_ID	X	subject	SITE_ID	FILE_ID	DX_GROUP	DSM_IV_TR	AGE_AT_SCAN	...	qc_notes_rater_1	qc_anat_rater_2	qc_anat_notes_rater_2	qc_func_rater_2	qc_func_notes_rater_2	qc_anat_rater_3	qc_anat_notes_rater_3	qc_func_rater_3	qc_func_notes_rater_3	SUB_IN_SMP
3	3	4	50005	4	50005	PITT	Pitt_0050005	1	1	13.73	...	NaN	OK	NaN	maybe	ic-parietal-cerebellum	OK	NaN	OK	NaN	0
19	19	20	50023	20	50023	PITT	Pitt_0050023	1	1	12.64	...	NaN	OK	NaN	maybe	ic-cerebellum_temporal_lobe	OK	NaN	OK	NaN	0
25	25	26	50029	26	50029	PITT	Pitt_0050029	1	1	11.40	...	NaN	maybe	skull-striping fail	fail	ic-parietal-cerebellum	OK	NaN	OK	NaN	0
32	32	33	50036	33	50036	PITT	Pitt_0050036	2	0	13.28	...	NaN	OK	NaN	maybe	ic-cerebellum	OK	NaN	OK	NaN	0
34	34	35	50038	35	50038	PITT	Pitt_0050038	2	0	13.95	...	NaN	OK	NaN	OK	NaN	OK	NaN	OK	NaN	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1065	1065	1066	51479	1066	51479	CALTECH	Caltech_0051479	2	0	20.00	...	NaN	OK	NaN	maybe	Ic-parietal-minor	OK	NaN	OK	NaN	0
1066	1066	1067	51480	1067	51480	CALTECH	Caltech_0051480	2	0	20.80	...	NaN	OK	NaN	OK	NaN	OK	NaN	OK	NaN	0
1068	1068	1069	51482	1069	51482	CALTECH	Caltech_0051482	2	0	21.10	...	NaN	OK	NaN	maybe	Ic-parietal-minor	OK	NaN	OK	NaN	0
1079	1079	1080	51493	1080	51493	CALTECH	Caltech_0051493	2	0	29.20	...	NaN	OK	NaN	maybe	Ic-parietal-minor	OK	NaN	OK	NaN	0
1110	1110	1111	51606	1111	51606	MAX_MUN	MaxMun_a_0051606	1	2	29.00	...	NaN	OK	NaN	maybe	ic-cerebellum	OK	NaN	OK	NaN	0

164 rows × 106 columns

male = Data_overview.loc[Data_overview['SEX']==1]
male

	Unnamed: 0	Unnamed: 0.1	SUB_ID	X	subject	SITE_ID	FILE_ID	DX_GROUP	DSM_IV_TR	AGE_AT_SCAN	...	qc_notes_rater_1	qc_anat_rater_2	qc_anat_notes_rater_2	qc_func_rater_2	qc_func_notes_rater_2	qc_anat_rater_3	qc_anat_notes_rater_3	qc_func_rater_3	qc_func_notes_rater_3	SUB_IN_SMP
0	0	1	50002	1	50002	PITT	no_filename	1	1	16.77	...	NaN	OK	NaN	fail	ic-parietal-cerebellum	OK	NaN	fail	ERROR #24	1
1	1	2	50003	2	50003	PITT	Pitt_0050003	1	1	24.45	...	NaN	OK	NaN	OK	NaN	OK	NaN	OK	NaN	1
2	2	3	50004	3	50004	PITT	Pitt_0050004	1	1	19.09	...	NaN	OK	NaN	OK	NaN	OK	NaN	OK	NaN	1
4	4	5	50006	5	50006	PITT	Pitt_0050006	1	1	13.37	...	NaN	OK	NaN	maybe	ic-parietal slight	OK	NaN	OK	NaN	1
5	5	6	50007	6	50007	PITT	Pitt_0050007	1	1	17.78	...	NaN	OK	NaN	maybe	ic-cerebellum_temporal_lob	OK	NaN	OK	NaN	1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1106	1106	1107	51582	1107	51582	SBL	SBL_0051582	1	2	31.00	...	NaN	OK	NaN	maybe	ic-cerebellum	OK	NaN	OK	NaN	0
1107	1107	1108	51583	1108	51583	SBL	SBL_0051583	1	2	35.00	...	NaN	OK	NaN	OK	ic-cerebellum-temporal_lobe	OK	NaN	OK	NaN	0
1108	1108	1109	51584	1109	51584	SBL	SBL_0051584	1	2	49.00	...	NaN	OK	NaN	maybe	vmpfc dropout	OK	NaN	OK	NaN	0
1109	1109	1110	51585	1110	51585	SBL	SBL_0051585	1	1	27.00	...	NaN	OK	NaN	maybe	ic-cerebellum-temporal_lobe	OK	NaN	OK	NaN	0
1111	1111	1112	51607	1112	51607	MAX_MUN	MaxMun_a_0051607	1	2	26.00	...	NaN	OK	NaN	maybe	ic-cerebellum	OK	NaN	OK	NaN	1

948 rows × 106 columns

There are 164 female participants, and 948 male participants. This again reiterates the uneven split we suggested above.

We can visualise this as well.

%matplotlib inline
import matplotlib.pyplot as plt

gender = Data_overview['SEX'].value_counts()
plt.figure(figsize=(7, 6))

ax = gender.plot(kind='bar', rot=0, color="c")
ax.set_title("Bar Graph of Gender", y = 1)
ax.set_xlabel('Gender')
ax.set_ylabel('Number of People')
ax.set_xticklabels(('Male', 'Female'))


for rect in ax.patches:
    y_value = rect.get_height()
    x_value = rect.get_x() + rect.get_width() / 2
    space = 1
    label = format(y_value)
    ax.annotate(label, (x_value, y_value), xytext=(0, space), textcoords="offset points", ha='center', va='bottom')    

group = Data_overview['DX_GROUP'].value_counts()
plt.figure(figsize=(7, 6))
    
ax = group.plot(kind='bar', rot=0, color="red")
ax.set_title("Bar Graph of Group", y = 1)
ax.set_xlabel('Group')
ax.set_ylabel('Number of People')
ax.set_xticklabels(('Control', 'ASD'))


for rect in ax.patches:
    y_value = rect.get_height()
    x_value = rect.get_x() + rect.get_width() / 2
    space = 1
    label = format(y_value)
    ax.annotate(label, (x_value, y_value), xytext=(0, space), textcoords="offset points", ha='center', va='bottom')    
plt.show()

../_images/fcf3873270a346cbef9cff58c8dc5d0f8a5dceff8d8f92328b33d3ecc383214f.png

../_images/f6f1a8cc060a46508b57987b2d2b78815883eb9eafce95f5ce7363762fc156c0.png

1.3. Matching#

As we saw above, we are working with very uneven groups in terms of gender. The ASD and control participant split seems fine thus this doesn’t require any alteration. However, in order to avoid any biases from the male group, we will create a matched pair sample instead, with one male participant being matched to a female participant based on whether they are in the ASD/control group and on their age. To do this we will use numpy.

import numpy as np

def find_matching (female_df, male_df):
    matched_list_asd, matched_list_control = [],[]
    
    for group in [1, 2]:
        female_group = female_df[female_df['DX_GROUP']==group]
        male_group = male_df[male_df['DX_GROUP']==group]
        
        age_male = male_group['AGE_AT_SCAN'].to_numpy()
        id_male = male_group['subject']
        used_male = []
    
        for index, row in female_group.iterrows():
            age_female = row['AGE_AT_SCAN']
            min_dif = np.argsort(np.abs(age_male - age_female))
            matched_idx = min_dif[0]

            if matched_idx in used_male:
                counter = 0
                while matched_idx in used_male: 
                    counter += 1
                    matched_idx = min_dif[counter]
                
            used_male.append(matched_idx)
                    
            if group == 1:
                matched_list_asd.append([row['subject'], id_male.iloc[matched_idx]])
            else:
                matched_list_control.append([row['subject'], id_male.iloc[matched_idx]])

    return matched_list_asd, matched_list_control

matched_list_asd, matched_list_control = find_matching(female, male)

Here we are defining a function which aims to create two matched lists. In both lists, one male participant is being assigned, or matched, with a female participant based on the group they are in and their age. So for every female participant, there is a male participant who is of the same age and in the same group.

print(matched_list_asd)

[[50005, 50243], [50023, 50148], [50029, 50823], [50057, 50191], [50119, 50686], [50125, 50652], [50127, 50411], [50185, 51236], [50276, 50273], [50278, 50805], [50284, 50286], [50285, 50298], [50300, 50310], [50302, 51214], [50309, 50982], [50319, 50120], [50321, 50124], [50407, 50405], [50604, 50155], [50615, 51175], [50619, 50517], [50620, 50618], [50621, 50608], [50623, 51239], [50624, 50626], [50627, 50978], [50644, 50711], [50648, 51320], [50650, 51582], [50743, 50323], [50744, 50613], [50749, 50235], [50792, 50802], [50795, 51348], [50796, 50122], [50798, 50791], [50952, 51011], [50953, 50800], [50954, 50522], [50955, 50294], [50956, 51300], [50957, 51220], [50958, 50144], [50959, 51459], [50960, 50651], [50961, 50499], [50962, 50003], [51162, 50609], [51164, 50793], [51167, 51165], [51170, 50993], [51207, 50183], [51215, 51202], [51219, 50972], [51226, 50965], [51228, 51166], [51230, 51472], [51242, 50510], [51321, 51330], [51325, 50708], [51460, 50497], [51462, 51465], [51463, 50477], [51471, 51019], [51606, 51322]]

print(matched_list_control)

[[50036, 50417], [50038, 50568], [50049, 50427], [50059, 51156], [50113, 50116], [50114, 51556], [50201, 50043], [50205, 50423], [50206, 50554], [50208, 50436], [50213, 51199], [50215, 51128], [50336, 51269], [50338, 51097], [50340, 50330], [50341, 50344], [50343, 51276], [50348, 51112], [50353, 51314], [50354, 50575], [50356, 50331], [50357, 51280], [50361, 50432], [50369, 50422], [50373, 50391], [50374, 50165], [50375, 50378], [50379, 51100], [50380, 50328], [50414, 51135], [50555, 51101], [50557, 51075], [50558, 51099], [50563, 50727], [50565, 50571], [50569, 50570], [50572, 51140], [50576, 50809], [50656, 50698], [50661, 50109], [50669, 51343], [50722, 50200], [50723, 50360], [50730, 50578], [50735, 50050], [50736, 50426], [50778, 50816], [50780, 51085], [50783, 50359], [50784, 51080], [50788, 50159], [50790, 51081], [50810, 51281], [50812, 50817], [50820, 50164], [51036, 51069], [51038, 51183], [51039, 50814], [51040, 50777], [51041, 51082], [51042, 51083], [51044, 50773], [51045, 51262], [51046, 50158], [51047, 51094], [51048, 50822], [51049, 51137], [51050, 51096], [51051, 51266], [51052, 50573], [51053, 50383], [51054, 50460], [51055, 51265], [51056, 51077], [51057, 50262], [51058, 50472], [51059, 51115], [51060, 51150], [51061, 51559], [51062, 51481], [51063, 51118], [51180, 50564], [51184, 50161], [51188, 50162], [51192, 50333], [51264, 51272], [51267, 50362], [51279, 51260], [51282, 50552], [51303, 51359], [51305, 51136], [51366, 51369], [51368, 50041], [51370, 51363], [51372, 51373], [51479, 51146], [51480, 51483], [51482, 50450], [51493, 51339]]

matched_list_asd_np =np.array(matched_list_asd)
matched_list_asd_np.shape

(65, 2)

matched_list_control_np =np.array(matched_list_control)
matched_list_control_np.shape

(99, 2)

We can see here that the list of controls is slightly longer than the list of participants with ASD. This deviation can later be controlled for during the analysis process. Since the difference is not too large, we shouldnt have too many issues. Note: when reading these lists we have to keep in mind that they are placed in arrays of two. This we have 130 participants with ASD, of which 65 are female and 65 are male, and 198 control participants of which 99 are male and 99 are female.

1.4. Visualizing#

It may be of interest to us to visualise this age matching in the two groups.

def get_ages(male_df, female_df, matched_list):
    id_male_asd = male_df['subject'].to_numpy()
    id_female_asd = female_df['subject'].to_numpy()
    age_male = male_df['AGE_AT_SCAN'].to_numpy()
    age_female = female_df['AGE_AT_SCAN'].to_numpy()
    selected_female_age = []
    for female_id in matched_list[:, 0]:
        selected_female_age.append(age_female[np.where(id_female_asd == female_id)[0]])
    selected_female_age = np.array(selected_female_age)[:, 0]
    selected_male_age = []                         
    for male_id in matched_list[:, 1]:
        selected_male_age.append(age_male[np.where(id_male_asd == male_id)[0]])
    selected_male_age = np.array(selected_male_age)[:, 0]
    return selected_male_age, selected_female_age

selected_male_age_asd, selected_female_age_asd = get_ages(male, female, np.array(matched_list_asd))
selected_male_age_control, selected_female_age_control = get_ages(male, female, np.array(matched_list_control))

import seaborn as sns

fig, axes = plt.subplots(1, 2, sharey=True, figsize=(10,5))

sns.histplot(selected_male_age_asd, color='red', ax=axes[0])
sns.histplot(selected_female_age_asd, ax=axes[0])
axes[0].set_xlabel('Age')
axes[0].set_title('Age distribution in ASD group')

sns.histplot(selected_male_age_control, color='red', ax=axes[1])
sns.histplot(selected_female_age_control, ax=axes[1])
axes[1].set_xlabel('Age')
axes[1].set_title('Age distribution in control group')

plt.legend(labels=['Male', 'Female'])

plt.show()

../_images/7f06d7cbaa7f3456dd7fcf4bfd846e4793753a48667cbbb0884c57a180742576.png

We may also want to visualise how well our matching went. To do this we need to subtract the male ages from the female ages in the various groups. In theory we should get as little difference in age as possible to assure that we matched as well as we could.

sns.histplot(selected_female_age_control - selected_male_age_control)
sns.histplot(selected_female_age_asd - selected_male_age_asd, color='red')

plt.xlabel('Age Difference (in years)')
plt.title('Matched age difference')
plt.legend(labels=['Control Group', 'ASD Group'])

plt.show()

../_images/1e7949d984dd2c24ac4bb2ab5638d4263e0b0d5b76b969a28acd226b5ad25f31.png

As hoped for, both the control and the ASD group are matched very well, with the largest age difference being 0,4 years of age. We can see by the distribution that most females were matched perfectly with a male participant.

1.5. Demographic information of matched sample#

It would now be valuable to assess what kind of participants we have in our matched sample.

joined_list = []
for list1 in (matched_list_asd):
     joined_list.extend(list1)
for list1 in (matched_list_control):
     joined_list.extend(list1)
        
len(joined_list)

For this we created a joined list of the matched ASD and control lists.

joined_list

selected_df = Data_overview.loc[Data_overview['subject'].isin(joined_list)]

selected_df

	Unnamed: 0	Unnamed: 0.1	SUB_ID	X	subject	SITE_ID	FILE_ID	DX_GROUP	DSM_IV_TR	AGE_AT_SCAN	...	qc_notes_rater_1	qc_anat_rater_2	qc_anat_notes_rater_2	qc_func_rater_2	qc_func_notes_rater_2	qc_anat_rater_3	qc_anat_notes_rater_3	qc_func_rater_3	qc_func_notes_rater_3	SUB_IN_SMP
1	1	2	50003	2	50003	PITT	Pitt_0050003	1	1	24.45	...	NaN	OK	NaN	OK	NaN	OK	NaN	OK	NaN	1
3	3	4	50005	4	50005	PITT	Pitt_0050005	1	1	13.73	...	NaN	OK	NaN	maybe	ic-parietal-cerebellum	OK	NaN	OK	NaN	0
19	19	20	50023	20	50023	PITT	Pitt_0050023	1	1	12.64	...	NaN	OK	NaN	maybe	ic-cerebellum_temporal_lobe	OK	NaN	OK	NaN	0
25	25	26	50029	26	50029	PITT	Pitt_0050029	1	1	11.40	...	NaN	maybe	skull-striping fail	fail	ic-parietal-cerebellum	OK	NaN	OK	NaN	0
32	32	33	50036	33	50036	PITT	Pitt_0050036	2	0	13.28	...	NaN	OK	NaN	maybe	ic-cerebellum	OK	NaN	OK	NaN	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1079	1079	1080	51493	1080	51493	CALTECH	Caltech_0051493	2	0	29.20	...	NaN	OK	NaN	maybe	Ic-parietal-minor	OK	NaN	OK	NaN	0
1080	1080	1081	51556	1081	51556	SBL	SBL_0051556	2	0	20.00	...	NaN	OK	NaN	maybe	ic-cerebellum-temporal_lobe	OK	NaN	OK	NaN	0
1083	1083	1084	51559	1084	51559	SBL	SBL_0051559	2	0	27.00	...	NaN	OK	NaN	maybe	ic-cerebellum	OK	NaN	OK	NaN	0
1106	1106	1107	51582	1107	51582	SBL	SBL_0051582	1	2	31.00	...	NaN	OK	NaN	maybe	ic-cerebellum	OK	NaN	OK	NaN	0
1110	1110	1111	51606	1111	51606	MAX_MUN	MaxMun_a_0051606	1	2	29.00	...	NaN	OK	NaN	maybe	ic-cerebellum	OK	NaN	OK	NaN	0

328 rows × 106 columns

selected_df.describe()

	Unnamed: 0	Unnamed: 0.1	SUB_ID	X	subject	DX_GROUP	DSM_IV_TR	AGE_AT_SCAN	SEX	HANDEDNESS_SCORES	...	func_fber	func_fwhm	func_dvars	func_outlier	func_quality	func_mean_fd	func_num_fd	func_perc_fd	func_gsr	SUB_IN_SMP
count	328.000000	328.000000	328.000000	328.000000	328.000000	328.000000	328.000000	328.000000	328.000000	105.000000	...	325.000000	325.000000	325.000000	325.000000	325.000000	325.000000	325.000000	325.000000	325.000000	328.000000
mean	570.121951	571.121951	50772.881098	571.121951	50772.881098	1.603659	-731.140244	15.695323	1.500000	-132.856667	...	106.021761	2.165669	1.090013	0.006249	0.012045	0.122540	25.021538	11.794128	0.026347	0.420732
std	303.453280	303.453280	423.357493	303.453280	423.357493	0.489884	2608.022581	7.064849	0.500764	1382.180332	...	46.466386	0.366371	0.116777	0.006495	0.011785	0.157514	43.286187	17.611243	0.021531	0.494431
min	1.000000	2.000000	50003.000000	2.000000	50003.000000	1.000000	-9999.000000	7.660000	1.000000	-9999.000000	...	33.836513	1.621089	0.709671	0.000197	0.001693	0.016117	0.000000	0.000000	-0.015812	0.000000
25%	300.250000	301.250000	50377.250000	301.250000	50377.250000	1.000000	0.000000	11.200000	1.000000	48.000000	...	72.096534	1.868385	1.011522	0.001310	0.006000	0.045449	1.000000	0.552486	0.010559	0.000000
50%	602.000000	603.000000	50786.000000	603.000000	50786.000000	2.000000	0.000000	13.865000	1.500000	71.000000	...	92.036699	2.057008	1.074603	0.005388	0.009267	0.077653	7.000000	3.821656	0.029558	0.000000
75%	812.750000	813.750000	51129.750000	813.750000	51129.750000	2.000000	1.000000	17.425000	2.000000	89.000000	...	133.575097	2.427396	1.162153	0.008089	0.014513	0.130854	25.000000	14.925373	0.040873	1.000000
max	1110.000000	1111.000000	51606.000000	1111.000000	51606.000000	2.000000	4.000000	46.000000	2.000000	100.000000	...	264.504674	3.489361	1.508322	0.035617	0.098614	1.324697	288.000000	91.735537	0.104225	1.000000

8 rows × 87 columns

We are working with 328 participants, with an average age of 15.7. Thanks to our matching, we have an exactly even split of female to male participants. The group split is not exactly perfect, but there is not such a large difference that it would cause major issues later on. We will also keep this distinction in mind and can use algorithms to correct for this uneven split later on.

2. Practice Exploring the data#

Now that we have matched our sample and gathered the relevant demographic information, it’s finally time to download the imaging data. When downloading data, it is always important to check what we are working with and whether the things we wish to do with it are also applicable.

2.1. Practice Download#

Before downloading all the files, we begin by loading just one participant.

import urllib.request

print('Beginning file download with urllib2...')

url = "https://s3.amazonaws.com/fcp-indi/data/Projects/ABIDE_Initiative/Outputs/ants/anat_thickness/Pitt_0050003_anat_thickness.nii.gz" 
urllib.request.urlretrieve(url, '/Users/gretivan/Desktop/Pitt_0050003_anat_thickness.nii.gz')

Beginning file download with urllib2...

('/Users/gretivan/Desktop/Pitt_0050003_anat_thickness.nii.gz',
 <http.client.HTTPMessage at 0x7f7cbb9d72b0>)

Using nibabel, we had a look at what the data actually looks like and the shape of it.

import nibabel as nb

Pitt_0050003 = nb.load('/Users/gretivan/Desktop/Pitt_0050003_anat_thickness.nii.gz')

data = Pitt_0050003.get_data()
data.shape

/Users/gretivan/miniconda3/envs/neuro_ai/lib/python3.7/site-packages/ipykernel_launcher.py:1: DeprecationWarning: get_data() is deprecated in favor of get_fdata(), which has a more predictable return type. To obtain get_data() behavior going forward, use numpy.asanyarray(img.dataobj).

* deprecated from version: 3.0
* Will raise <class 'nibabel.deprecator.ExpiredDeprecationError'> as of version: 5.0
  """Entry point for launching an IPython kernel.

(216, 256, 291)

This shows us that the images we are working with have 3 Dimensions to them.

2.2. Visualizing#

It would be interesting to actually look at and get a detailed idea of the images. To do this we use nilearn as a tool to get a better and more interactive look at the gray matter images. When working with gray matter images, we are only looking at the cortical thickness of the start of the cortex to the beginning of the white matter.

from nilearn.plotting import plot_anat, view_img
view_img(Pitt_0050003, bg_img = False, cmap = "plasma")

The different colors on this image, different shades of pink and orange, represent thicker or thinner areas of the gray matter. Overall, this image gives a good representation of the data we would like to work with.

2.3. Deciding on and loading the Atlas#

In order to provide a more clear view of the coritcal thickness data, we decided to implement an atlas that partialises our images into 148 different areas and calculates the mean cortical thickness of this area. This allows for easier comparison between the different groups to establish whether differences exist.

To do this, we decided to use the Destrieux atlas (2010) for this partialisation. We used the Destrieux atlas as it provides a more fine grained partialisation of brain areas compared to other atlases, such as the Desikan-Killiany atlas, that are widely used for Autism research. A very significant, and pivotal study in our decision making was carried out by Haar et al., 2016 who also applied the Destrieux atlas to the ABIDE I dataset to assess anatomical abnormalities in ASD. They reported finding several significantly larger cortical thicknesses in some areas in ASD groups when compared to controls.

Now that we have picked which Atlas we wish to use to map and mask our data for easier analysis, we must download it. First of all, its important to have all the packages we wish to use in this proces downloaded.

from nilearn.image import load_img
from nilearn.maskers import NiftiLabelsMasker
from nilearn.datasets import fetch_atlas_destrieux_2009
from nilearn.plotting import plot_roi, plot_stat_map
from numpy import recarray

We can now fetch our Atlas.

destrieux_dataset = fetch_atlas_destrieux_2009(lateralized=True)

/Users/gretivan/miniconda3/envs/neuro_ai/lib/python3.7/site-packages/nilearn/datasets/atlas.py:284: UserWarning: `legacy_format` will default to `False` in release 0.11. Dataset fetchers will then return pandas dataframes by default instead of recarrays.
  warnings.warn(_LEGACY_FORMAT_MSG)

destrieux_atlas = load_img(destrieux_dataset.maps)

destrieux_atlas.shape

(76, 93, 76)

The shape of the atlas is also in 3 dimensional form which is important for our data, since it is also provided in 3 dimensions.

plot_roi(destrieux_atlas)

/Users/gretivan/miniconda3/envs/neuro_ai/lib/python3.7/site-packages/nilearn/plotting/img_plotting.py:348: FutureWarning: Default resolution of the MNI template will change from 2mm to 1mm in version 0.10.0
  anat_img = load_mni152_template()

<nilearn.plotting.displays._slicers.OrthoSlicer at 0x7f7cda4fa208>

../_images/7e2753fc03cdf04a5e2d6daa8049531ad09ebd4fcbee2c0977bf17fbfb681f2a.png

np.unique(destrieux_atlas.dataobj)

array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,
        13,  14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,
        26,  27,  28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,
        39,  40,  41,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,
        53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,  65,
        66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,  78,
        79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,  91,
        92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103, 104,
       105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 118,
       119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131,
       132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144,
       145, 146, 147, 148, 149, 150], dtype=int32)

As provided in the information about the atlas, we can see here that there are 148 partialisations of gray matter regions. The Atlas also provides us with labels for these regions.

lables = destrieux_dataset.labels
lables

rec.array([(  0, 'Background'), (  1, 'L G_and_S_frontomargin'),
           (  2, 'L G_and_S_occipital_inf'),
           (  3, 'L G_and_S_paracentral'), (  4, 'L G_and_S_subcentral'),
           (  5, 'L G_and_S_transv_frontopol'),
           (  6, 'L G_and_S_cingul-Ant'),
           (  7, 'L G_and_S_cingul-Mid-Ant'),
           (  8, 'L G_and_S_cingul-Mid-Post'),
           (  9, 'L G_cingul-Post-dorsal'),
           ( 10, 'L G_cingul-Post-ventral'), ( 11, 'L G_cuneus'),
           ( 12, 'L G_front_inf-Opercular'),
           ( 13, 'L G_front_inf-Orbital'),
           ( 14, 'L G_front_inf-Triangul'), ( 15, 'L G_front_middle'),
           ( 16, 'L G_front_sup'), ( 17, 'L G_Ins_lg_and_S_cent_ins'),
           ( 18, 'L G_insular_short'), ( 19, 'L G_occipital_middle'),
           ( 20, 'L G_occipital_sup'), ( 21, 'L G_oc-temp_lat-fusifor'),
           ( 22, 'L G_oc-temp_med-Lingual'),
           ( 23, 'L G_oc-temp_med-Parahip'), ( 24, 'L G_orbital'),
           ( 25, 'L G_pariet_inf-Angular'),
           ( 26, 'L G_pariet_inf-Supramar'), ( 27, 'L G_parietal_sup'),
           ( 28, 'L G_postcentral'), ( 29, 'L G_precentral'),
           ( 30, 'L G_precuneus'), ( 31, 'L G_rectus'),
           ( 32, 'L G_subcallosal'), ( 33, 'L G_temp_sup-G_T_transv'),
           ( 34, 'L G_temp_sup-Lateral'),
           ( 35, 'L G_temp_sup-Plan_polar'),
           ( 36, 'L G_temp_sup-Plan_tempo'), ( 37, 'L G_temporal_inf'),
           ( 38, 'L G_temporal_middle'), ( 39, 'L Lat_Fis-ant-Horizont'),
           ( 40, 'L Lat_Fis-ant-Vertical'), ( 41, 'L Lat_Fis-post'),
           ( 42, 'L Medial_wall'), ( 43, 'L Pole_occipital'),
           ( 44, 'L Pole_temporal'), ( 45, 'L S_calcarine'),
           ( 46, 'L S_central'), ( 47, 'L S_cingul-Marginalis'),
           ( 48, 'L S_circular_insula_ant'),
           ( 49, 'L S_circular_insula_inf'),
           ( 50, 'L S_circular_insula_sup'),
           ( 51, 'L S_collat_transv_ant'),
           ( 52, 'L S_collat_transv_post'), ( 53, 'L S_front_inf'),
           ( 54, 'L S_front_middle'), ( 55, 'L S_front_sup'),
           ( 56, 'L S_interm_prim-Jensen'),
           ( 57, 'L S_intrapariet_and_P_trans'),
           ( 58, 'L S_oc_middle_and_Lunatus'),
           ( 59, 'L S_oc_sup_and_transversal'),
           ( 60, 'L S_occipital_ant'), ( 61, 'L S_oc-temp_lat'),
           ( 62, 'L S_oc-temp_med_and_Lingual'),
           ( 63, 'L S_orbital_lateral'), ( 64, 'L S_orbital_med-olfact'),
           ( 65, 'L S_orbital-H_Shaped'), ( 66, 'L S_parieto_occipital'),
           ( 67, 'L S_pericallosal'), ( 68, 'L S_postcentral'),
           ( 69, 'L S_precentral-inf-part'),
           ( 70, 'L S_precentral-sup-part'), ( 71, 'L S_suborbital'),
           ( 72, 'L S_subparietal'), ( 73, 'L S_temporal_inf'),
           ( 74, 'L S_temporal_sup'), ( 75, 'L S_temporal_transverse'),
           ( 76, 'R G_and_S_frontomargin'),
           ( 77, 'R G_and_S_occipital_inf'),
           ( 78, 'R G_and_S_paracentral'), ( 79, 'R G_and_S_subcentral'),
           ( 80, 'R G_and_S_transv_frontopol'),
           ( 81, 'R G_and_S_cingul-Ant'),
           ( 82, 'R G_and_S_cingul-Mid-Ant'),
           ( 83, 'R G_and_S_cingul-Mid-Post'),
           ( 84, 'R G_cingul-Post-dorsal'),
           ( 85, 'R G_cingul-Post-ventral'), ( 86, 'R G_cuneus'),
           ( 87, 'R G_front_inf-Opercular'),
           ( 88, 'R G_front_inf-Orbital'),
           ( 89, 'R G_front_inf-Triangul'), ( 90, 'R G_front_middle'),
           ( 91, 'R G_front_sup'), ( 92, 'R G_Ins_lg_and_S_cent_ins'),
           ( 93, 'R G_insular_short'), ( 94, 'R G_occipital_middle'),
           ( 95, 'R G_occipital_sup'), ( 96, 'R G_oc-temp_lat-fusifor'),
           ( 97, 'R G_oc-temp_med-Lingual'),
           ( 98, 'R G_oc-temp_med-Parahip'), ( 99, 'R G_orbital'),
           (100, 'R G_pariet_inf-Angular'),
           (101, 'R G_pariet_inf-Supramar'), (102, 'R G_parietal_sup'),
           (103, 'R G_postcentral'), (104, 'R G_precentral'),
           (105, 'R G_precuneus'), (106, 'R G_rectus'),
           (107, 'R G_subcallosal'), (108, 'R G_temp_sup-G_T_transv'),
           (109, 'R G_temp_sup-Lateral'),
           (110, 'R G_temp_sup-Plan_polar'),
           (111, 'R G_temp_sup-Plan_tempo'), (112, 'R G_temporal_inf'),
           (113, 'R G_temporal_middle'), (114, 'R Lat_Fis-ant-Horizont'),
           (115, 'R Lat_Fis-ant-Vertical'), (116, 'R Lat_Fis-post'),
           (117, 'R Medial_wall'), (118, 'R Pole_occipital'),
           (119, 'R Pole_temporal'), (120, 'R S_calcarine'),
           (121, 'R S_central'), (122, 'R S_cingul-Marginalis'),
           (123, 'R S_circular_insula_ant'),
           (124, 'R S_circular_insula_inf'),
           (125, 'R S_circular_insula_sup'),
           (126, 'R S_collat_transv_ant'),
           (127, 'R S_collat_transv_post'), (128, 'R S_front_inf'),
           (129, 'R S_front_middle'), (130, 'R S_front_sup'),
           (131, 'R S_interm_prim-Jensen'),
           (132, 'R S_intrapariet_and_P_trans'),
           (133, 'R S_oc_middle_and_Lunatus'),
           (134, 'R S_oc_sup_and_transversal'),
           (135, 'R S_occipital_ant'), (136, 'R S_oc-temp_lat'),
           (137, 'R S_oc-temp_med_and_Lingual'),
           (138, 'R S_orbital_lateral'), (139, 'R S_orbital_med-olfact'),
           (140, 'R S_orbital-H_Shaped'), (141, 'R S_parieto_occipital'),
           (142, 'R S_pericallosal'), (143, 'R S_postcentral'),
           (144, 'R S_precentral-inf-part'),
           (145, 'R S_precentral-sup-part'), (146, 'R S_suborbital'),
           (147, 'R S_subparietal'), (148, 'R S_temporal_inf'),
           (149, 'R S_temporal_sup'), (150, 'R S_temporal_transverse')],
          dtype=[('index', '<i8'), ('name', 'O')])

2.4. Checking the atlas space#

It’s important to check whether the data and the Atlas are present in the same space. If they are not, we can not use a masker as there are no brain images that the masker can be mapped onto.

plot_roi(Pitt_0050003, bg_img=destrieux_atlas)

<nilearn.plotting.displays._slicers.OrthoSlicer at 0x7f7cdb0d71d0>

../_images/a20318c91b0a150b267f2554a244ba5d546b9f881fbedf0cc57ffad08c711fdf.png

This image highlights the issue we mentioned above - the atlas and the test image are not in fact in the same space. This means that the atlas is not mapping onto the brain image. So we must move the image into the correct space.

2.5. Adjusting the image#

In order to allign the atlas and the image, we must:

get a different version of the thickness data
get the transformations we need to transform the thickness data from the given participant’s space to the template space, specifically MNI152NLin2009cAsym

It becomes very clear here why we explore a small set, or even just one image, of a dataset before downloading everything and working with that.

We have to start by getting a different version of cortical thickness data than we had initially planned to download.

import urllib.request

url = "https://fcp-indi.s3.amazonaws.com/data/Projects/ABIDE/Outputs/mindboggle_swf/mindboggle/ants_subjects/sub-0050003/antsCorticalThickness.nii.gz" 
urllib.request.urlretrieve(url, '/Users/gretivan/Desktop/sub-0050003_desc-corticalthickness.nii.gz')

('/Users/gretivan/Desktop/sub-0050003_desc-corticalthickness.nii.gz',
 <http.client.HTTPMessage at 0x7f7cdc0fe6d8>)

Next we must get the transformations from the participant to the template space.

url = "https://fcp-indi.s3.amazonaws.com/data/Projects/ABIDE/Outputs/fmriprep/fmriprep/sub-0050003/anat/sub-0050003_from-T1w_to-MNI152NLin2009cAsym_mode-image_xfm.h5" 
urllib.request.urlretrieve(url, '/Users/gretivan/Desktop/sub-0050003_from-T1w_to-MNI152NLin2009cAsym_mode-image_xfm.h5')

('/Users/gretivan/Desktop/sub-0050003_from-T1w_to-MNI152NLin2009cAsym_mode-image_xfm.h5',
 <http.client.HTTPMessage at 0x7f7cdc0d1208>)

Next we must download the template image.

from templateflow import api as tflow

mni152 = tflow.get('MNI152NLin2009cAsym', desc=None, resolution=1,
                    suffix='T1w', extension='nii.gz')
mni152

PosixPath('/Users/gretivan/.cache/templateflow/tpl-MNI152NLin2009cAsym/tpl-MNI152NLin2009cAsym_res-01_T1w.nii.gz')

Now that we have downloaded a different version of the image and transformations, we can apply these transformations using ANTs (Advanced Normalisation Tool). During the downloading process and application of the antsApplyTransforms, we came across some issues. The Ants tool would not show up as an available command as bash would not access the bashrc file. In order to resolve this issue, we added an extra export path to bash with the path where the Ants file is located.

import nipype
from nipype.interfaces.ants import ApplyTransforms

%%bash
export PATH=/Users/gretivan/Desktop/ants-2.4.3/bin:$PATH
echo $PATH

/Users/gretivan/Desktop/ants-2.4.3/bin:/Users/gretivan/miniconda3/envs/neuro_ai/bin:/Users/gretivan/miniconda3/condabin:/usr/local/bin:/System/Cryptexes/App/usr/bin:/usr/bin:/bin:/usr/sbin:/sbin:/Library/Apple/usr/bin

%%bash
/Users/gretivan/Desktop/ants-2.4.3/bin/antsApplyTransforms  -i /Users/gretivan/Desktop/sub-0050003_desc-corticalthickness.nii.gz -r /Users/gretivan/.cache/templateflow/tpl-MNI152NLin2009cAsym/tpl-MNI152NLin2009cAsym_res-01_T1w.nii.gz -t /Users/gretivan/Desktop/sub-0050003_from-T1w_to-MNI152NLin2009cAsym_mode-image_xfm.h5 -o /Users/gretivan/Desktop/sub-0050003_space-MNI152NLin2009cAsym_desc-corticalthickness.nii.gz

from nilearn.plotting import plot_img

fig = plot_img('/Users/gretivan/Desktop/sub-0050003_space-MNI152NLin2009cAsym_desc-corticalthickness.nii.gz', cmap='magma')
fig.add_contours(destrieux_atlas, cmap='Greys')

../_images/f09ffdb1ace5f33a572067458e165900ac35147dff3f1028152bd1c4515ea29f.png

Here we can see that the masker and the image are now in the same space, meaning we can now apply the masker.

2.6. Testing the masker#

The destrieux atlas can be used as a masker for our data to allow for an assessment of 2 dimensional cortical thickness data as opposed to a 3 dimensional image. It will help us when looking at differences between groups.

destrieux_masker = NiftiLabelsMasker(labels_img=destrieux_atlas)
thickness_sub0050003 = destrieux_masker.fit_transform('/Users/gretivan/Desktop/sub-0050003_space-MNI152NLin2009cAsym_desc-corticalthickness.nii.gz')

thickness_sub0050003.shape

(1, 148)

Here we can see that our image is now in a 2 dimensional shape, with 148 mean cortical thickness volumes.

thickness_sub0050003

array([[1.24944175, 2.34536168, 0.606836  , 1.71352572, 1.52545434,
        3.10993156, 2.04731399, 2.10898342, 2.92694597, 1.52362547,
        1.25300568, 1.78683702, 1.36531509, 1.33183801, 1.72617632,
        1.79656362, 2.45268168, 3.06566965, 1.91948329, 1.06945715,
        3.09177802, 1.51964456, 2.92252828, 1.61312081, 1.33889796,
        1.75681046, 0.82938553, 0.79790724, 1.27660216, 1.80350527,
        2.29007522, 0.92637947, 1.33526913, 1.97557859, 1.29265578,
        1.15743216, 2.56120038, 2.35720902, 1.17724812, 1.49941028,
        1.3729409 , 1.41701773, 2.84152904, 1.25315129, 0.46842935,
        1.03995316, 1.60925862, 0.85085033, 1.84187833, 2.91596348,
        2.02335056, 1.37771389, 0.84202682, 1.40546209, 0.86230999,
        0.8225973 , 0.99084249, 1.25135441, 2.01259242, 3.08581815,
        2.71363081, 0.60009495, 0.88043531, 1.34770937, 1.15595373,
        0.83335493, 0.80230146, 1.0742383 , 0.84154005, 3.80625478,
        2.01734593, 1.77281437, 1.52858828, 0.67798723, 1.59052564,
        2.10532643, 0.65951737, 1.84252687, 1.8731977 , 3.08255791,
        2.57705834, 2.65432732, 3.29420721, 1.85932626, 1.59324833,
        1.62434517, 2.00253108, 1.48929939, 2.11547331, 1.88203491,
        2.99583286, 3.50206556, 1.76242735, 1.30028175, 2.86001846,
        1.4645331 , 2.90693984, 1.60345748, 1.42553496, 1.89063294,
        0.6637651 , 0.705717  , 1.05332134, 1.9460822 , 2.74310166,
        0.94061779, 1.54846976, 1.69223849, 0.97717156, 0.58586031,
        2.83781352, 2.73770981, 1.36209438, 0.71621789, 1.02328903,
        1.78896477, 2.82955619, 1.35781833, 0.37849333, 1.09557431,
        2.22725458, 0.87094703, 1.8186979 , 2.36792727, 2.17386297,
        1.19262718, 1.21617019, 1.7952098 , 1.36532848, 0.96187317,
        1.19106197, 1.24348792, 1.53188043, 3.10959993, 2.29012877,
        0.73042764, 1.13302745, 1.48995839, 1.39482021, 0.95580911,
        1.01340645, 0.78417804, 0.92455626, 2.66533118, 2.79238291,
        1.73645045, 1.42436385, 0.54438221]])

3. Exploring the full dataset#

Now that we have established what kind of data we are working with, what atlas we are wanting to use, and what kind of preprocessing is needed to make sure that the masker works, we can download the full matched sample.

3.1. Downloading the matched dataset#

failed_download = []

for subject in joined_list:
    print(subject, end=", ")
    try:
        # Downloading different cortical thickness images
        url = "https://fcp-indi.s3.amazonaws.com/data/Projects/ABIDE/Outputs/mindboggle_swf/mindboggle/ants_subjects/sub-00%s/antsCorticalThickness.nii.gz" %(subject) 
        urllib.request.urlretrieve(url, '/Users/gretivan/Desktop/Neuro_data/sub-00%s_desc-corticalthickness.nii.gz'%(subject))
        # Downloading transformation template for thickness images 
        url = "https://fcp-indi.s3.amazonaws.com/data/Projects/ABIDE/Outputs/fmriprep/fmriprep/sub-00%s/anat/sub-00%s_from-T1w_to-MNI152NLin2009cAsym_mode-image_xfm.h5" %(subject, subject)
        urllib.request.urlretrieve(url, '/Users/gretivan/Desktop/Neuro_data/sub-00%s_from-T1w_to-MNI152NLin2009cAsym_mode-image_xfm.h5'%(subject))
    except urllib.error.HTTPError:
        failed_download.append(subject)
        
print(failed_download)

50005, 50243, 50023, 50148, 50029, 50823, 50057, 50191, 50119, 50686, 50125, 50652, 50127, 50411, 50185, 51236, 50276, 50273, 50278, 50805, 50284, 50286, 50285, 50298, 50300, 50310, 50302, 51214, 50309, 50982, 50319, 50120, 50321, 50124, 50407, 50405, 50604, 50155, 50615, 51175, 50619, 50517, 50620, 50618, 50621, 50608, 50623, 51239, 50624, 50626, 50627, 50978, 50644, 50711, 50648, 51320, 50650, 51582, 50743, 50323, 50744, 50613, 50749, 50235, 50792, 50802, 50795, 51348, 50796, 50122, 50798, 50791, 50952, 51011, 50953, 50800, 50954, 50522, 50955, 50294, 50956, 51300, 50957, 51220, 50958, 50144, 50959, 51459, 50960, 50651, 50961, 50499, 50962, 50003, 51162, 50609, 51164, 50793, 51167, 51165, 51170, 50993, 51207, 50183, 51215, 51202, 51219, 50972, 51226, 50965, 51228, 51166, 51230, 51472, 51242, 50510, 51321, 51330, 51325, 50708, 51460, 50497, 51462, 51465, 51463, 50477, 51471, 51019, 51606, 51322, 50036, 50417, 50038, 50568, 50049, 50427, 50059, 51156, 50113, 50116, 50114, 51556, 50201, 50043, 50205, 50423, 50206, 50554, 50208, 50436, 50213, 51199, 50215, 51128, 50336, 51269, 50338, 51097, 50340, 50330, 50341, 50344, 50343, 51276, 50348, 51112, 50353, 51314, 50354, 50575, 50356, 50331, 50357, 51280, 50361, 50432, 50369, 50422, 50373, 50391, 50374, 50165, 50375, 50378, 50379, 51100, 50380, 50328, 50414, 51135, 50555, 51101, 50557, 51075, 50558, 51099, 50563, 50727, 50565, 50571, 50569, 50570, 50572, 51140, 50576, 50809, 50656, 50698, 50661, 50109, 50669, 51343, 50722, 50200, 50723, 50360, 50730, 50578, 50735, 50050, 50736, 50426, 50778, 50816, 50780, 51085, 50783, 50359, 50784, 51080, 50788, 50159, 50790, 51081, 50810, 51281, 50812, 50817, 50820, 50164, 51036, 51069, 51038, 51183, 51039, 50814, 51040, 50777, 51041, 51082, 51042, 51083, 51044, 50773, 51045, 51262, 51046, 50158, 51047, 51094, 51048, 50822, 51049, 51137, 51050, 51096, 51051, 51266, 51052, 50573, 51053, 50383, 51054, 50460, 51055, 51265, 51056, 51077, 51057, 50262, 51058, 50472, 51059, 51115, 51060, 51150, 51061, 51559, 51062, 51481, 51063, 51118, 51180, 50564, 51184, 50161, 51188, 50162, 51192, 50333, 51264, 51272, 51267, 50362, 51279, 51260, 51282, 50552, 51303, 51359, 51305, 51136, 51366, 51369, 51368, 50041, 51370, 51363, 51372, 51373, 51479, 51146, 51480, 51483, 51482, 50450, 51493, 51339, [50309, 50517, 50323, 50522, 50499, 51242, 50510, 50497, 50477, 50049, 50423, 50436, 50432, 50722, 50460, 50472, 50450]

During the download we noticed some files were unable to be downloaded. These are shown in a failed_download list.

print(failed_download)

[50309, 50517, 50323, 50522, 50499, 51242, 50510, 50497, 50477, 50049, 50423, 50436, 50432, 50722, 50460, 50472, 50450]

failed_download = [50309, 50517, 50323, 50522, 50499, 51242, 50510, 50497, 50477, 50049, 50423, 50436, 50432, 50722, 50460, 50472, 50450]

if download is not in/ isin continue, if not append ---> loop to get successful download, logcial opperator

successful_download = [50005, 50243, 50023, 50148, 50029, 50823, 50057, 50191, 50119, 50686, 50125, 50652, 50127, 50411, 50185, 51236, 50276, 50273, 50278, 50805, 50284, 50286, 50285, 50298, 50300, 50310, 50302, 51214, 50982, 50319, 50120, 50321, 50124, 50407, 50405, 50604, 50155, 50615, 51175, 50619, 50620, 50618, 50621, 50608, 50623, 51239, 50624, 50626, 50627, 50978, 50644, 50711, 50648, 51320, 50650, 51582, 50743, 50744, 50613, 50749, 50235, 50792, 50802, 50795, 51348, 50796, 50122, 50798, 50791, 50952, 51011, 50953, 50800, 50954, 50955, 50294, 50956, 51300, 50957, 51220, 50958, 50144, 50959, 51459, 50960, 50651, 50961, 50962, 50003, 51162, 50609, 51164, 50793, 51167, 51165, 51170, 50993, 51207, 50183, 51215, 51202, 51219, 50972, 51226, 50965, 51228, 51166, 51230, 51472, 51321, 51330, 51325, 50708, 51460, 51462, 51465, 51463, 51471, 51019, 51606, 51322, 50036, 50417, 50038, 50568, 50427, 50059, 51156, 50113, 50116, 50114, 51556, 50201, 50043, 50205, 50206, 50554, 50208, 50213, 51199, 50215, 51128, 50336, 51269, 50338, 51097, 50340, 50330, 50341, 50344, 50343, 51276, 50348, 51112, 50353, 51314, 50354, 50575, 50356, 50331, 50357, 51280, 50361, 50369, 50422, 50373, 50391, 50374, 50165, 50375, 50378, 50379, 51100, 50380, 50328, 50414, 51135, 50555, 51101, 50557, 51075, 50558, 51099, 50563, 50727, 50565, 50571, 50569, 50570, 50572, 51140, 50576, 50809, 50656, 50698, 50661, 50109, 50669, 51343, 50200, 50723, 50360, 50730, 50578, 50735, 50050, 50736, 50426, 50778, 50816, 50780, 51085, 50783, 50359, 50784, 51080, 50788, 50159, 50790, 51081, 50810, 51281, 50812, 50817, 50820, 50164, 51036, 51069, 51038, 51183, 51039, 50814, 51040, 50777, 51041, 51082, 51042, 51083, 51044, 50773, 51045, 51262, 51046, 50158, 51047, 51094, 51048, 50822, 51049, 51137, 51050, 51096, 51051, 51266, 51052, 50573, 51053, 50383, 51054,  51055, 51265, 51056, 51077, 51057, 50262, 51058, 51059, 51115, 51060, 51150, 51061, 51559, 51062, 51481, 51063, 51118, 51180, 50564, 51184, 50161, 51188, 50162, 51192, 50333, 51264, 51272, 51267, 50362, 51279, 51260, 51282, 50552, 51303, 51359, 51305, 51136, 51366, 51369, 51368, 50041, 51370, 51363, 51372, 51373, 51479, 51146, 51480, 51483, 51482, 51493, 51339]

len(successful_download)

len(failed_download)

Even though we our dataset was again reduced by 17 datapoints, or brain images, as a result to them not being able to download, we decided to, instead of rematching the 17 that are now left unmatched, apply an algorithm later on that takes these group differences into account as previously mentioned.

3.2. Rechecking the demographic information#

Since 17 files could not be downloaded, we are left with a smaller sample of 311 participants. Lets recheck the demographics.

successful_demographics = Data_overview.loc[Data_overview['subject'].isin(successful_download)]

successful_demographics

	Unnamed: 0	Unnamed: 0.1	SUB_ID	X	subject	SITE_ID	FILE_ID	DX_GROUP	DSM_IV_TR	AGE_AT_SCAN	...	qc_notes_rater_1	qc_anat_rater_2	qc_anat_notes_rater_2	qc_func_rater_2	qc_func_notes_rater_2	qc_anat_rater_3	qc_anat_notes_rater_3	qc_func_rater_3	qc_func_notes_rater_3	SUB_IN_SMP
1	1	2	50003	2	50003	PITT	Pitt_0050003	1	1	24.45	...	NaN	OK	NaN	OK	NaN	OK	NaN	OK	NaN	1
3	3	4	50005	4	50005	PITT	Pitt_0050005	1	1	13.73	...	NaN	OK	NaN	maybe	ic-parietal-cerebellum	OK	NaN	OK	NaN	0
19	19	20	50023	20	50023	PITT	Pitt_0050023	1	1	12.64	...	NaN	OK	NaN	maybe	ic-cerebellum_temporal_lobe	OK	NaN	OK	NaN	0
25	25	26	50029	26	50029	PITT	Pitt_0050029	1	1	11.40	...	NaN	maybe	skull-striping fail	fail	ic-parietal-cerebellum	OK	NaN	OK	NaN	0
32	32	33	50036	33	50036	PITT	Pitt_0050036	2	0	13.28	...	NaN	OK	NaN	maybe	ic-cerebellum	OK	NaN	OK	NaN	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1079	1079	1080	51493	1080	51493	CALTECH	Caltech_0051493	2	0	29.20	...	NaN	OK	NaN	maybe	Ic-parietal-minor	OK	NaN	OK	NaN	0
1080	1080	1081	51556	1081	51556	SBL	SBL_0051556	2	0	20.00	...	NaN	OK	NaN	maybe	ic-cerebellum-temporal_lobe	OK	NaN	OK	NaN	0
1083	1083	1084	51559	1084	51559	SBL	SBL_0051559	2	0	27.00	...	NaN	OK	NaN	maybe	ic-cerebellum	OK	NaN	OK	NaN	0
1106	1106	1107	51582	1107	51582	SBL	SBL_0051582	1	2	31.00	...	NaN	OK	NaN	maybe	ic-cerebellum	OK	NaN	OK	NaN	0
1110	1110	1111	51606	1111	51606	MAX_MUN	MaxMun_a_0051606	1	2	29.00	...	NaN	OK	NaN	maybe	ic-cerebellum	OK	NaN	OK	NaN	0

311 rows × 106 columns

successful_demographics.describe()

	Unnamed: 0	Unnamed: 0.1	SUB_ID	X	subject	DX_GROUP	DSM_IV_TR	AGE_AT_SCAN	SEX	HANDEDNESS_SCORES	...	func_fber	func_fwhm	func_dvars	func_outlier	func_quality	func_mean_fd	func_num_fd	func_perc_fd	func_gsr	SUB_IN_SMP
count	311.000000	311.000000	311.000000	311.000000	311.000000	311.000000	311.000000	311.000000	311.000000	94.000000	...	309.000000	309.000000	309.000000	309.000000	309.000000	309.000000	309.000000	309.000000	309.000000	311.000000
mean	580.099678	581.099678	50788.311897	581.099678	50788.311897	1.610932	-771.135048	15.599116	1.514469	-155.904255	...	107.505379	2.174188	1.090451	0.006264	0.011391	0.116046	23.035599	11.142548	0.025744	0.408360
std	306.012396	306.012396	426.120741	306.012396	426.120741	0.488324	2672.792656	7.120146	0.500596	1459.767555	...	46.756526	0.367685	0.119160	0.006303	0.010334	0.140554	39.766551	16.604553	0.021717	0.492323
min	1.000000	2.000000	50003.000000	2.000000	50003.000000	1.000000	-9999.000000	7.660000	1.000000	-9999.000000	...	33.836513	1.621089	0.709671	0.000197	0.001693	0.016117	0.000000	0.000000	-0.015812	0.000000
25%	297.500000	298.500000	50374.500000	298.500000	50374.500000	1.000000	0.000000	11.080000	1.000000	44.500000	...	72.303412	1.872158	1.008803	0.001406	0.005826	0.045322	1.000000	0.552486	0.010012	0.000000
50%	614.000000	615.000000	50798.000000	615.000000	50798.000000	2.000000	0.000000	13.780000	2.000000	69.000000	...	94.165911	2.080066	1.076446	0.005738	0.009097	0.073796	7.000000	3.654485	0.028792	0.000000
75%	821.500000	822.500000	51138.500000	822.500000	51138.500000	2.000000	1.000000	17.355000	2.000000	89.000000	...	134.382319	2.432877	1.165317	0.008300	0.014300	0.128553	24.000000	14.012739	0.040802	1.000000
max	1110.000000	1111.000000	51606.000000	1111.000000	51606.000000	2.000000	4.000000	46.000000	2.000000	100.000000	...	264.504674	3.489361	1.508322	0.035617	0.098614	1.224343	288.000000	91.735537	0.104225	1.000000

8 rows × 87 columns

We can see here that we still have a relatively even group splits, with a slightly higher average age.

3.3. Apply the transformations#

The next thing to do, now that we have a list of successfully downloaded participant IDs, is to apply the transformation to all of them to create a 3rd transformed file. To do this, we use a loop with the syntax from above where we initially applied the transformation to one participant.

successful_list_str = ' '.join([str(subject) for subject in successful_download])

%%bash -s "$successful_list_str"

for subject in $1
do
echo $subject
antsApplyTransforms -i /Users/gretivan/Desktop/Neuro_data/sub-00${subject}_desc-corticalthickness.nii.gz -r /Users/gretivan/.cache/templateflow/tpl-MNI152NLin2009cAsym/tpl-MNI152NLin2009cAsym_res-01_T1w.nii.gz -t /Users/gretivan/Desktop/Neuro_data/sub-00${subject}_from-T1w_to-MNI152NLin2009cAsym_mode-image_xfm.h5 -o /Users/gretivan/Desktop/Neuro_data/sub-00${subject}_space-MNI152NLin2009cAsym_desc-corticalthickness.nii.gz
done

After downloading the data, and transforming it to the correct space, we need to apply the masker and save it in a dictionary.

output = {}
for subject in successful_download:
    print(subject, end=',')
    
        # Apply the masker to each subject
    destrieux_masker = NiftiLabelsMasker(labels_img=destrieux_atlas)
    temp = destrieux_masker.fit_transform('/Users/gretivan/Desktop/Neuro_data/sub-00%s_space-MNI152NLin2009cAsym_desc-corticalthickness.nii.gz'%(subject))
    output[subject] = temp
    np.save('/Users/gretivan/Desktop/Neuro_data/sub-00%s_destrieux-corticalthickness.npy'%(subject), temp)
        

50005,50243,50023,50148,50029,50823,50057,50191,50119,50686,50125,50652,50127,50411,50185,51236,50276,50273,50278,50805,50284,50286,50285,50298,50300,50310,50302,51214,50982,50319,50120,50321,50124,50407,50405,50604,50155,50615,51175,50619,50620,50618,50621,50608,50623,51239,50624,50626,50627,50978,50644,50711,50648,51320,50650,51582,50743,50744,50613,50749,50235,50792,50802,50795,51348,50796,50122,50798,50791,50952,51011,50953,50800,50954,50955,50294,50956,51300,50957,51220,50958,50144,50959,51459,50960,50651,50961,50962,50003,51162,50609,51164,50793,51167,51165,51170,50993,51207,50183,51215,51202,51219,50972,51226,50965,51228,51166,51230,51472,51321,51330,51325,50708,51460,51462,51465,51463,51471,51019,51606,51322,50036,50417,50038,50568,50427,50059,51156,50113,50116,50114,51556,50201,50043,50205,50206,50554,50208,50213,51199,50215,51128,50336,51269,50338,51097,50340,50330,50341,50344,50343,51276,50348,51112,50353,51314,50354,50575,50356,50331,50357,51280,50361,50369,50422,50373,50391,50374,50165,50375,50378,50379,51100,50380,50328,50414,51135,50555,51101,50557,51075,50558,51099,50563,50727,50565,50571,50569,50570,50572,51140,50576,50809,50656,50698,50661,50109,50669,51343,50200,50723,50360,50730,50578,50735,50050,50736,50426,50778,50816,50780,51085,50783,50359,50784,51080,50788,50159,50790,51081,50810,51281,50812,50817,50820,50164,51036,51069,51038,51183,51039,50814,51040,50777,51041,51082,51042,51083,51044,50773,51045,51262,51046,50158,51047,51094,51048,50822,51049,51137,51050,51096,51051,51266,51052,50573,51053,50383,51054,51055,51265,51056,51077,51057,50262,51058,51059,51115,51060,51150,51061,51559,51062,51481,51063,51118,51180,50564,51184,50161,51188,50162,51192,50333,51264,51272,51267,50362,51279,51260,51282,50552,51303,51359,51305,51136,51366,51369,51368,50041,51370,51363,51372,51373,51479,51146,51480,51483,51482,51493,51339,

print(output)

{50005: array([[1.88431166, 1.76320336, 1.11390663, 1.48948011, 1.57417584,
        2.97995139, 2.38282191, 2.2512736 , 3.48374256, 1.81977405,
        0.75322368, 1.95353456, 2.23619082, 1.48573905, 1.86495464,
        2.11882744, 2.40894353, 3.53348546, 1.8843735 , 0.93950251,
        2.15076486, 1.46824549, 2.35921589, 2.09702396, 1.6438221 ,
        1.73707352, 1.101166  , 0.93737361, 1.41206387, 2.19841133,
        2.00448107, 0.91124679, 1.14893239, 2.66672103, 1.22886184,
        1.47507838, 2.32726174, 2.73099436, 1.6916697 , 1.66477398,
        1.27619732, 0.89819511, 2.73064589, 1.34446161, 0.40929411,
        1.20095507, 1.83660777, 0.8791849 , 1.77808816, 3.15361791,
        2.12237219, 1.37161697, 1.17744452, 1.27603078, 0.82204156,
        1.10068347, 1.00103868, 0.80338873, 2.3565658 , 2.75643984,
        2.54019814, 0.72897244, 0.82760495, 1.62765494, 1.3111408 ,
        1.2412859 , 0.84518014, 1.00472426, 1.28115383, 3.01723173,
        2.1098516 , 1.59073786, 1.47489439, 0.76920345, 1.42911178,
        2.01864916, 0.78280345, 1.65597352, 1.4567783 , 2.87560631,
        2.47006803, 2.8263264 , 3.22036491, 2.38823411, 0.7447412 ,
        1.62682614, 2.67187359, 1.48997045, 1.68472608, 1.93147282,
        2.91287701, 3.31982944, 2.17808211, 0.99075413, 2.35610062,
        1.6574913 , 2.54403497, 2.04103376, 1.7690707 , 1.34863032,
        1.32571107, 0.87249712, 1.21244157, 2.06107629, 2.50203163,
        0.98995161, 1.03789072, 1.84821398, 0.87284654, 0.46802253,
        2.67001555, 2.4034679 , 1.67371225, 1.50354043, 0.96811162,
        0.81573768, 2.09612185, 1.15442232, 0.33019121, 1.11907021,
        2.04323525, 0.77237977, 1.67932414, 1.99111835, 1.75347401,
        1.04704805, 1.03618329, 1.41607287, 1.32487179, 1.11080661,
        1.13752847, 0.98633388, 2.40689126, 2.86795179, 2.41174409,
        0.71872598, 1.50676689, 1.38303572, 1.26867382, 1.33245066,
        1.09218291, 1.12928418, 1.0018315 , 2.63499489, 2.48648666,
        1.81394695, 1.37799052, 0.45037188]]), 50243: array([[1.80849437, 2.75828028, 1.37867002, 2.16474805, 2.00925282,
        3.39912881, 2.55034392, 2.68584871, 3.45581853, 2.27535755,
        1.90943288, 2.37449875, 2.07904582, 1.93026198, 1.58561178,
        2.1370297 , 3.34264041, 5.35831056, 2.12171644, 1.17410924,
        3.53375855, 2.65134282, 3.17374657, 2.34284715, 2.16690578,
        1.9378148 , 0.95448794, 1.01613618, 1.52665109, 2.67348406,
        2.77033101, 1.19428935, 1.76854636, 2.72964951, 2.13971957,
        1.46800032, 2.64486254, 2.58806961, 1.64460564, 1.07878429,
        1.62735745, 1.80095483, 3.5046053 , 1.83894174, 0.68541147,
        1.21201791, 2.77672948, 1.5807112 , 2.43822723, 3.66795086,
        2.31538692, 1.46994186, 1.45521745, 1.52471774, 2.09078087,
        1.10672067, 1.41537378, 1.32881023, 3.36317706, 3.61266318,
        3.16223917, 0.9482731 , 1.82348762, 1.71215548, 1.90093823,
        1.20955797, 0.918246  , 1.01384519, 1.23764114, 3.17752394,
        3.15981945, 1.69220469, 2.14800312, 0.80316445, 1.53147321,
        2.47702827, 1.18208253, 2.48575743, 1.86538957, 3.42782407,
        3.34939724, 3.46379747, 4.08142567, 2.57802462, 1.79979805,
        2.74163392, 2.57851257, 1.93825436, 1.78144385, 2.16863307,
        3.79436907, 5.12789204, 2.21232634, 1.18285435, 3.28763877,
        2.33846572, 3.38224558, 2.48867353, 2.0504955 , 2.04481388,
        1.05138903, 1.10959677, 1.65102262, 2.84372023, 3.47885432,
        1.2310076 , 1.7145911 , 2.97215929, 2.04619322, 0.98268141,
        2.67805382, 3.00556146, 2.44622228, 2.28246076, 1.44780214,
        1.92613335, 3.07171011, 1.85082579, 0.4948157 , 2.07535925,
        2.73843223, 1.63599971, 2.62627762, 3.40226863, 2.49389944,
        1.53037329, 1.37616908, 1.65065393, 1.89433626, 0.86452718,
        1.6477372 , 1.24917244, 2.49894603, 3.05174179, 2.70839667,
        1.12362108, 2.06346087, 2.08616535, 1.60105972, 1.47143684,
        1.00412873, 1.40807594, 1.31005808, 2.6920003 , 3.45916177,
        2.02937485, 1.80621445, 0.56001236]]), 50023: array([[1.63949224, 2.28692356, 1.09877051, 1.94024674, 1.22021006,
        3.27141422, 2.55652839, 2.3467391 , 3.03447959, 1.57385781,
        1.31620151, 2.28514285, 2.32534996, 2.06579216, 1.54994428,
        2.08703369, 2.23062423, 4.39945651, 1.93245959, 1.26848337,
        3.1427097 , 1.92663114, 2.69465901, 2.29789601, 2.37867442,
        1.90849428, 1.27886185, 1.04561357, 1.32651122, 2.2062929 ,
        2.53541315, 0.53127623, 1.59096936, 3.09912602, 1.6908334 ,
        1.31080125, 2.60761739, 3.33928887, 1.99342596, 1.35788769,
        1.52646264, 1.56848647, 3.22587687, 1.56233403, 0.59348901,
        1.33013564, 2.1197036 , 1.19741519, 2.3455421 , 2.73481767,
        1.35467781, 1.49418983, 1.16189898, 1.2632335 , 1.99981671,
        1.23580073, 0.66345446, 1.25014111, 2.86182518, 3.16625187,
        2.70121528, 0.55049071, 1.3717731 , 2.01121876, 2.03980484,
        0.9081234 , 0.9056036 , 1.51164541, 0.98403985, 3.42093469,
        2.28963186, 1.52482202, 2.05891605, 0.4500483 , 1.83463382,
        2.4524393 , 1.25452733, 1.67616294, 1.53284696, 2.9834458 ,
        3.08643624, 3.00405952, 3.47439943, 2.00750031, 2.17409787,
        2.18616408, 2.60298662, 2.23162536, 1.60427198, 2.22412671,
        3.21993671, 4.18821869, 1.9485981 , 1.61431349, 2.91389383,
        2.2206659 , 3.41339069, 2.10970091, 2.53804263, 1.94083661,
        1.12176839, 1.00909712, 1.22613955, 2.35477802, 2.62112739,
        1.14907833, 1.39918487, 2.65906632, 1.30672465, 0.94674341,
        2.65852001, 3.37143319, 2.45230236, 1.96612907, 1.23025104,
        1.64043725, 3.139154  , 1.59078867, 0.53757872, 1.86425854,
        2.45560944, 1.24641838, 2.34030988, 2.74467793, 2.33580663,
        1.49674222, 1.32658467, 1.46110296, 1.81332477, 1.23185945,
        1.67528091, 1.18061046, 2.20849358, 3.84228328, 2.59036175,
        1.4961519 , 1.33139227, 1.58726161, 1.91419265, 0.94670518,
        1.22564593, 1.31326141, 0.90322789, 2.50602284, 3.21746488,
        1.87751459, 1.98503694, 0.3734417 ]]), 50148: array([[1.17504465, 2.28238638, 1.47958549, 2.31616127, 1.19049469,
        2.45089776, 1.85578302, 2.0150547 , 3.0440797 , 2.10867703,
        1.9043238 , 2.04792978, 1.57632624, 1.53403576, 1.69111301,
        2.12871385, 2.31970749, 3.43758246, 2.42263034, 1.47938245,
        2.91751204, 2.61212549, 2.7677258 , 2.1944293 , 1.90388885,
        1.98835317, 1.39211599, 1.07917529, 1.70711983, 2.30076924,
        2.568081  , 1.35934892, 1.50521064, 2.18178183, 1.52846568,
        1.22202864, 2.98625267, 2.97405508, 1.25997461, 0.92904372,
        1.47830885, 2.78554376, 2.77394842, 1.56707136, 0.63551822,
        1.24971639, 1.80596751, 1.10483661, 1.93997024, 3.25014977,
        2.11040286, 1.34183739, 1.06064086, 1.43185511, 1.71781422,
        1.16088123, 1.4539839 , 1.31697621, 2.56361368, 3.04045273,
        2.53069471, 0.35368363, 1.46020273, 1.62756273, 1.64757531,
        0.94333977, 1.21743342, 1.26369097, 1.44028361, 3.52411417,
        2.18311396, 1.91190243, 1.90500461, 0.56700105, 1.50508496,
        2.21233052, 1.24629723, 1.75684724, 1.57947801, 2.86567633,
        2.29478631, 2.5475712 , 3.47759222, 2.58061308, 2.03759316,
        2.12721748, 1.84237003, 1.4240689 , 1.7372084 , 1.88164447,
        3.16178985, 3.65389584, 2.51452482, 1.62631597, 3.20637566,
        2.5199409 , 3.27944136, 2.26505786, 2.84269747, 2.03402712,
        1.1523204 , 1.10386733, 1.48768384, 2.33310353, 2.51936975,
        1.96076187, 1.12244446, 1.95332616, 1.4818957 , 0.83400598,
        2.84466612, 2.6333835 , 1.64090995, 1.77611729, 1.19397951,
        2.86031199, 2.86350916, 1.59210333, 0.50045742, 1.48065334,
        2.33629161, 1.32983965, 1.84584604, 3.12772875, 2.07618359,
        0.98392815, 1.62492815, 1.50665856, 1.37899901, 1.63040645,
        1.58562031, 1.54140102, 1.44272699, 3.24394326, 2.2839638 ,
        1.17639968, 1.59478934, 1.7166541 , 1.92019391, 1.21273966,
        1.04967628, 1.06351438, 1.12000467, 2.16395294, 2.47160152,
        1.45997093, 1.64480309, 0.40426942]]), 50029: array([[1.92657054, 2.84863746, 1.26545148, 2.43251578, 2.82713741,
        3.11982748, 2.67395021, 2.96705601, 3.50666617, 2.36336418,
        1.33290833, 3.11452721, 2.62625831, 2.41249878, 2.17726265,
        2.52162629, 3.7226844 , 4.80394758, 2.43255362, 1.12597472,
        3.14472572, 2.16509249, 2.86144661, 2.61637703, 2.45208869,
        2.16398543, 1.08077156, 1.14326039, 1.67061199, 2.62446735,
        2.92434232, 1.60098518, 1.78952635, 2.85579991, 1.70350835,
        1.76405826, 2.7770328 , 3.14560018, 1.72613233, 2.48257646,
        1.79136671, 2.436956  , 3.34396554, 1.45712217, 0.69662566,
        1.95854285, 2.8967866 , 1.28161401, 2.69061255, 3.33415817,
        2.39393269, 1.66086395, 2.01154717, 1.70220278, 1.56270283,
        1.21761323, 1.7269678 , 1.1581445 , 3.19936939, 2.99404227,
        3.12116255, 1.37459997, 1.6493203 , 2.17984464, 1.63066632,
        1.06490474, 1.46502335, 1.80694572, 1.49884201, 3.56561841,
        2.72106461, 1.95959226, 1.90743576, 0.93553853, 1.80986215,
        2.80812504, 1.14326581, 2.38240736, 2.34325077, 2.96214736,
        2.93361808, 3.35588335, 4.13884578, 2.74739541, 1.79392518,
        2.10460093, 2.79183024, 2.14802393, 2.03651699, 2.37834672,
        4.64872193, 4.94803136, 2.60061129, 1.32430072, 3.5849856 ,
        2.59634662, 2.91516484, 2.55762124, 2.18257793, 2.41547216,
        0.92901363, 1.2433497 , 1.6593932 , 2.82146016, 3.12027283,
        1.95766757, 1.79040726, 2.39753393, 1.3828408 , 1.47020773,
        2.92506937, 3.23254035, 2.1207993 , 2.18415509, 1.32827493,
        2.03061824, 3.01698711, 1.68593233, 0.61141396, 2.04749053,
        2.87565039, 1.45550753, 2.60444204, 2.74875812, 2.09120475,
        1.42488959, 1.82026173, 1.97548684, 2.2878595 , 1.33716581,
        1.67616801, 1.31964138, 2.9510206 , 3.24326262, 2.84539549,
        1.57293889, 1.87558307, 1.92344006, 1.88763604, 1.11387364,
        1.80439437, 1.43887501, 1.41811958, 2.93488184, 3.36570194,
        2.05574925, 1.88478247, 0.74944003]]), 50823: array([[1.34194979, 2.39510833, 1.31879899, 2.39734208, 1.36405917,
        2.46062593, 1.91920917, 2.27311598, 3.33498612, 2.92678163,
        1.51230338, 1.99267162, 1.87200207, 1.40306775, 1.5130221 ,
        1.80928959, 3.92010204, 4.741474  , 2.74192677, 1.1741915 ,
        3.20056653, 2.89678185, 3.33510405, 2.32220039, 2.40351297,
        2.11451347, 1.367065  , 1.39137592, 1.60473935, 2.32633783,
        2.63521575, 1.20321226, 1.78594709, 2.68995919, 1.81631286,
        1.43738982, 2.98786346, 3.45771264, 1.19565326, 1.52886674,
        2.00365643, 1.74734061, 3.77445151, 1.67620197, 0.6557687 ,
        1.51892151, 2.57311672, 1.40433631, 2.43398905, 3.34021172,
        1.43519996, 1.26132422, 0.94121167, 1.41063751, 1.58576016,
        1.22624909, 1.69139017, 1.55841509, 3.34766154, 3.19600517,
        3.46397895, 0.87538015, 1.6434041 , 1.71515631, 1.69286955,
        1.04999815, 1.12632083, 1.343465  , 1.25091634, 3.19591662,
        2.64327094, 1.95789055, 2.03912314, 0.9161457 , 1.51303043,
        2.77167202, 1.25853335, 2.4772877 , 1.56628817, 2.8852852 ,
        2.64018271, 2.83522393, 4.00994614, 3.9986002 , 2.17635917,
        2.36100371, 2.30535192, 1.81177446, 1.52060299, 1.89687911,
        3.78178003, 4.40604751, 2.53626049, 1.45177456, 4.1628798 ,
        3.50873219, 3.60519329, 2.58676331, 2.23969319, 1.98612421,
        1.1363196 , 1.10245089, 1.46212575, 2.44647389, 3.52941845,
        1.98182055, 1.94291901, 2.67563561, 1.60558963, 1.21501607,
        3.50100446, 3.70814128, 1.66265181, 2.00011739, 1.44978056,
        2.3011892 , 3.57882097, 2.03120409, 0.56428178, 1.89068806,
        2.53416402, 1.58955501, 2.25792697, 2.50248571, 1.51087258,
        1.2604458 , 1.6095572 , 1.4126339 , 1.07433566, 1.14462591,
        1.76902598, 1.58432247, 2.02581166, 3.05211504, 3.06793496,
        1.39988857, 1.67965133, 2.0731686 , 1.77759604, 1.29026833,
        1.15961639, 1.16838779, 1.06602635, 2.2262974 , 3.43623413,
        2.39267005, 1.80662329, 0.8146125 ]]), 50057: array([[1.61195405, 2.12943522, 1.56794214, 1.90485877, 2.32897802,
        2.86616076, 2.49249344, 2.89805565, 2.91784538, 1.52629701,
        1.1727422 , 2.46252879, 2.34523159, 1.969659  , 2.17731981,
        2.55572649, 3.24803075, 4.29313372, 2.15747179, 0.98396161,
        3.0932413 , 2.11533761, 2.54924666, 2.28814647, 2.17300801,
        1.98494703, 1.4749056 , 1.26234437, 1.94092689, 2.39197002,
        2.49798507, 0.7168852 , 1.38665426, 2.95262193, 1.60725832,
        1.41828724, 2.39538065, 3.08920052, 2.0525691 , 2.37846103,
        1.41279288, 1.32943166, 2.88484274, 1.58830093, 0.64960102,
        1.69430324, 2.15897461, 1.28274675, 2.01256709, 2.69221628,
        1.86475858, 1.75329072, 1.61948191, 1.68671593, 2.0481351 ,
        0.96418611, 0.94362083, 1.17594463, 2.27489495, 3.13475969,
        3.29631932, 1.59207143, 1.11855412, 2.08014036, 1.66438968,
        0.96119426, 0.97924319, 1.73972231, 1.19776047, 3.75248496,
        2.10524448, 1.57996791, 2.02664308, 0.64610384, 2.10489523,
        2.16815781, 1.71170007, 1.6673323 , 2.33668362, 3.36534475,
        3.04356444, 3.39070439, 3.66062382, 2.57712493, 1.81753789,
        2.22141965, 1.7546347 , 2.40711483, 2.10481757, 2.55086267,
        3.44759423, 4.00250289, 2.29684901, 1.42051671, 2.97291913,
        2.7858318 , 2.70750719, 2.55909207, 2.36514218, 2.03412536,
        1.71589141, 1.3049231 , 1.75154384, 2.6360587 , 3.2292063 ,
        1.41626817, 1.38812741, 2.5823877 , 1.4998161 , 0.84781995,
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        0.80684858, 1.12514059, 0.90863253, 2.61851477, 2.79782714,
        1.45854767, 1.65304365, 0.46560058]]), 51493: array([[1.22967529, 1.88009921, 0.34975561, 1.64369144, 1.42304036,
        3.66319972, 2.89659852, 2.53423394, 3.22974829, 1.8990793 ,
        1.18918919, 1.54505356, 1.5547592 , 1.05233132, 1.00107667,
        1.65709356, 2.57825824, 3.49411681, 1.56223888, 0.61510893,
        2.5153333 , 1.81137548, 2.66289294, 1.86788317, 1.26509639,
        1.63398098, 0.46685423, 0.38298245, 0.62232241, 2.33483092,
        3.13633152, 0.88615342, 1.61259133, 1.84809942, 1.20051921,
        0.8231803 , 2.38402507, 2.79438722, 1.24178452, 1.42006357,
        1.73229466, 1.82314714, 2.23864111, 1.57599979, 0.31675599,
        0.99850883, 2.10345329, 0.96728822, 1.79017564, 2.55095768,
        1.62197408, 1.14398138, 0.89702711, 1.38701554, 1.62129608,
        1.04889907, 1.14559841, 0.70538073, 2.34623291, 2.21434525,
        2.45489495, 0.68411642, 1.56325696, 1.59793276, 1.71232485,
        1.08027193, 0.7949738 , 1.50386362, 1.10686809, 3.77406358,
        3.06338433, 1.38326459, 1.46529481, 0.84398432, 1.15046482,
        1.5454672 , 0.30664763, 1.5434284 , 1.41129629, 2.90309201,
        2.79441456, 2.08488567, 2.97923415, 2.30390929, 2.31982352,
        1.68161567, 2.03286071, 1.05604444, 1.17446823, 1.36710236,
        3.45177438, 4.51063197, 2.38432989, 0.73615142, 2.63921989,
        2.12844904, 2.25702198, 2.15001163, 1.38355136, 1.46157638,
        0.44468318, 0.48615994, 0.66290886, 2.16827541, 2.58345821,
        1.02308576, 2.00276316, 1.7456808 , 0.7755473 , 0.48242751,
        2.59300704, 3.0544558 , 2.22580008, 1.87480898, 1.2368041 ,
        1.5322297 , 1.7806299 , 1.76184697, 0.43709303, 1.47868872,
        2.32276085, 0.80320781, 1.99160502, 2.82071684, 1.2354776 ,
        0.98088659, 1.15346294, 1.5579653 , 1.14806905, 0.97856367,
        1.51652386, 1.1562678 , 2.88951711, 3.12299386, 2.13900235,
        0.99779559, 0.99638204, 1.72362856, 1.49757692, 1.11339602,
        0.87243035, 1.17402082, 0.83547792, 1.55986295, 3.51479852,
        1.4096632 , 1.64484124, 0.79495671]]), 51339: array([[0.99775495, 1.72303729, 0.70173744, 1.43644712, 1.142354  ,
        2.82967248, 2.22776539, 1.70735122, 2.78232755, 1.59910719,
        0.89216227, 1.62647098, 1.47189427, 1.08286718, 1.2320886 ,
        1.48339437, 2.86484927, 3.62210136, 1.56480376, 0.98707888,
        2.47428275, 1.49707331, 2.48485356, 1.6903648 , 1.18091392,
        1.333168  , 0.65189997, 0.55447226, 1.08736822, 1.72365945,
        2.06960499, 0.62735531, 1.29511985, 1.74740254, 1.38975854,
        0.91928162, 2.38613073, 2.15976879, 1.42128264, 1.81826973,
        1.15680754, 1.54499415, 2.70610624, 1.01586452, 0.37984449,
        0.90875844, 1.51170843, 0.9386461 , 1.82127419, 2.89965426,
        2.16997181, 1.08797611, 0.57314906, 0.92892184, 0.827166  ,
        0.68619502, 1.41220684, 0.97563248, 1.9910084 , 2.71733493,
        2.22905685, 0.1529719 , 1.12067639, 1.34084042, 1.09946623,
        0.68905229, 0.72230869, 1.15916337, 0.93051135, 3.05031524,
        2.14349023, 1.30593378, 1.58381832, 0.49743661, 0.81950785,
        2.42166369, 0.50924481, 1.35677914, 1.03432285, 1.70125931,
        1.75793308, 1.84928667, 3.11404602, 2.38709289, 1.07122309,
        1.7175372 , 1.66067756, 0.84231412, 0.98626931, 1.13340074,
        3.17005167, 3.59661046, 2.30101445, 0.72162465, 2.45814199,
        1.48359319, 2.70063849, 1.65269017, 1.49465075, 1.47155443,
        0.50071362, 0.47509108, 0.78712861, 1.80385242, 2.27918813,
        0.98336871, 1.23632159, 1.67160112, 0.86421657, 0.53566927,
        2.9983591 , 2.41540093, 1.26825498, 1.27277369, 1.07537652,
        1.65212734, 2.25852228, 1.32539749, 0.3536504 , 1.44941961,
        2.27497524, 0.85755917, 1.79744975, 3.10851726, 2.52596066,
        0.98665581, 0.76785183, 1.06555726, 1.31460666, 0.74076284,
        1.10345033, 0.87799138, 1.48987664, 3.08037795, 1.78332227,
        0.6367967 , 1.05703079, 1.22271528, 0.94933635, 0.88423591,
        0.81367264, 0.85955514, 0.53462062, 1.62617757, 2.73970821,
        1.30041255, 1.39716117, 0.31992387]])}

4. Data Analysis#

4.1. Correlation#

Now that we have preprocessed our data (downloaded, transformed and masked), we can start with the data analysis. The first area of interest is to visualize the correlations between our average cortical thicknesses.

To do this, we have to first reshape our dictioary as it is currently in 3 Dimensions which is not accepted by the dataframe from dict function.

copied_output = output.copy()

for key, value in copied_output.items():
    copied_output[key] = np.array(value).flatten().tolist()
print(copied_output)

{50005: [1.8843116580679127, 1.7632033588890814, 1.11390663255268, 1.4894801092224226, 1.574175841995615, 2.9799513923711474, 2.382821913369561, 2.2512735977345826, 3.4837425619036035, 1.8197740537508602, 0.7532236795371391, 1.9535345605040175, 2.2361908239181294, 1.485739048249093, 1.8649546351436141, 2.118827436960781, 2.4089435277446603, 3.5334854557176376, 1.8843734971781836, 0.9395025142424569, 2.150764860386891, 1.4682454869911885, 2.3592158852990788, 2.0970239572027447, 1.6438221042611592, 1.737073522745288, 1.1011660034546134, 0.9373736117930688, 1.4120638667356598, 2.1984113274997346, 2.004481070574635, 0.9112467934564467, 1.148932387732501, 2.6667210341107745, 1.2288618361351162, 1.4750783777781058, 2.3272617361609744, 2.7309943604106635, 1.6916697011564357, 1.6647739767688061, 1.2761973197284189, 0.8981951145167861, 2.7306458871438313, 1.3444616084749008, 0.4092941108964016, 1.2009550717519109, 1.8366077674266679, 0.8791849047463367, 1.7780881584983663, 3.153617907381963, 2.1223721922490104, 1.371616969776691, 1.1774445205243034, 1.2760307839021094, 0.8220415631712754, 1.1006834696034493, 1.0010386763202737, 0.8033887269878459, 2.3565657981477437, 2.75643984165125, 2.540198141446746, 0.7289724442363267, 0.8276049495639127, 1.627654938170468, 1.311140804143875, 1.2412858996464973, 0.845180142502347, 1.0047242582881448, 1.2811538294780882, 3.017231725659939, 2.1098515984704047, 1.590737858762355, 1.474894392005965, 0.7692034521474427, 1.429111776392551, 2.0186491565502114, 0.7828034464961681, 1.6559735193801832, 1.4567783012280622, 2.875606309014374, 2.470068033816509, 2.826326395498921, 3.22036490546012, 2.388234108072286, 0.7447412029604263, 1.6268261396602823, 2.6718735857855536, 1.4899704542507175, 1.6847260793406926, 1.9314728223626103, 2.912877007489229, 3.3198294401490767, 2.1780821075306487, 0.9907541303005538, 2.35610062084423, 1.6574912974227016, 2.544034965930729, 2.041033758606008, 1.7690707003029416, 1.348630321961504, 1.3257110668122527, 0.8724971170776399, 1.2124415744788721, 2.061076291176655, 2.5020316308718824, 0.9899516087219142, 1.0378907202126346, 1.8482139835623825, 0.8728465442593414, 0.4680225307639712, 2.6700155480070467, 2.4034679029080377, 1.673712246639955, 1.5035404286188154, 0.9681116158788996, 0.815737684902019, 2.096121849163459, 1.1544223176488786, 0.33019120517720346, 1.119070207174023, 2.0432352544281556, 0.7723797684188813, 1.679324137738256, 1.9911183493895102, 1.7534740073414716, 1.047048049042083, 1.0361832888636202, 1.416072871535283, 1.3248717904788163, 1.1108066097151603, 1.13752846646902, 0.986333884536939, 2.406891261622998, 2.867951791165917, 2.4117440914944117, 0.7187259772494072, 1.5067668911278347, 1.3830357184926965, 1.26867382230458, 1.3324506600947257, 1.0921829147519861, 1.1292841799087245, 1.001831500130941, 2.63499489423849, 2.4864866644745924, 1.8139469546643359, 1.3779905234568604, 0.45037187657689265], 50243: [1.8084943717874986, 2.758280278306597, 1.3786700156103642, 2.164748052546385, 2.009252816276499, 3.3991288125211443, 2.5503439160035217, 2.685848714613944, 3.455818525018309, 2.2753575533102692, 1.9094328765871622, 2.3744987516883507, 2.07904582372025, 1.930261980802281, 1.5856117825970848, 2.1370297022953704, 3.342640405601658, 5.358310561564244, 2.1217164418711847, 1.1741092412528171, 3.5337585458894014, 2.651342817028102, 3.173746573911427, 2.342847149494237, 2.1669057824989486, 1.9378147999830744, 0.9544879396647079, 1.0161361826238633, 1.5266510949507215, 2.6734840613507425, 2.770331014191508, 1.1942893531827838, 1.768546355001088, 2.729649511944122, 2.1397195731254213, 1.4680003236323678, 2.644862538357504, 2.588069610465212, 1.6446056398736455, 1.0787842886796346, 1.6273574454323214, 1.8009548304896423, 3.5046052975590083, 1.8389417423505556, 0.6854114737719512, 1.2120179108115032, 2.776729481431133, 1.5807111980025979, 2.4382272316629243, 3.6679508567154455, 2.3153869158359623, 1.4699418585817567, 1.4552174472580435, 1.5247177445336557, 2.090780873304396, 1.1067206688143074, 1.4153737822238146, 1.3288102316648869, 3.363177061363633, 3.61266317626097, 3.162239169515785, 0.9482731030820754, 1.8234876240849218, 1.7121554754756227, 1.9009382300135573, 1.2095579725083783, 0.91824599998649, 1.013845191076048, 1.237641137788598, 3.177523937614658, 3.15981944896883, 1.6922046925302854, 2.148003117008499, 0.8031644459812761, 1.5314732059189058, 2.4770282749020387, 1.1820825261459642, 2.4857574279149435, 1.8653895664789146, 3.427824074428355, 3.3493972441317728, 3.4637974731458554, 4.081425668710908, 2.57802461642681, 1.799798052933329, 2.741633915960509, 2.5785125694453312, 1.938254362982166, 1.7814438546333042, 2.168633073866173, 3.7943690734093254, 5.1278920439869955, 2.21232633652084, 1.182854352449723, 3.2876387748152944, 2.3384657178033628, 3.3822455814800096, 2.488673531206425, 2.0504954973551004, 2.044813877782709, 1.0513890294815662, 1.1095967667807618, 1.6510226160676513, 2.843720226403891, 3.478854319880132, 1.2310075980341546, 1.7145910979840056, 2.972159289079229, 2.0461932229910955, 0.9826814112295699, 2.6780538226406705, 3.005561456256473, 2.4462222773202926, 2.2824607637047527, 1.44780214424699, 1.9261333520194237, 3.0717101144359895, 1.8508257861343689, 0.49481570255327295, 2.0753592497831272, 2.7384322346876906, 1.6359997121305907, 2.626277619213628, 3.4022686316971953, 2.493899435432952, 1.530373287817175, 1.3761690807275309, 1.6506539347702376, 1.8943362620818402, 0.8645271751624526, 1.6477372007240905, 1.2491724382613023, 2.498946030607761, 3.051741792058543, 2.70839667252259, 1.1236210793790444, 2.063460866640828, 2.08616534669749, 1.6010597211717488, 1.4714368433169394, 1.0041287332513438, 1.4080759445825806, 1.310058078327929, 2.6920003029014996, 3.459161769500739, 2.0293748548960138, 1.806214451916199, 0.5600123606349394], 50023: [1.6394922415186768, 2.2869235626205464, 1.0987705052481256, 1.9402467362233502, 1.2202100567507908, 3.2714142172777354, 2.5565283885618433, 2.3467391024511137, 3.034479592066339, 1.5738578058363586, 1.316201508197937, 2.2851428507774454, 2.3253499592068536, 2.0657921561932846, 1.5499442768304603, 2.0870336885651395, 2.2306242324800825, 4.399456513002578, 1.9324595931420019, 1.2684833717985984, 3.14270969938994, 1.9266311440759496, 2.6946590085735567, 2.2978960136587054, 2.378674420201267, 1.9084942783663097, 1.2788618545198989, 1.045613567208586, 1.3265112197388724, 2.206292897445813, 2.535413149639275, 0.5312762330493978, 1.5909693572565027, 3.0991260185286755, 1.690833397736234, 1.3108012545311176, 2.60761738550955, 3.3392888669837384, 1.993425960190523, 1.357887691646423, 1.526462640300237, 1.5684864737386779, 3.2258768700107066, 1.5623340328874493, 0.5934890068871564, 1.3301356353424725, 2.1197036026409792, 1.197415190453926, 2.3455421021839373, 2.7348176749040563, 1.354677807633796, 1.4941898253388153, 1.1618989835508131, 1.2632335001422688, 1.9998167101386473, 1.2358007273493565, 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2.659066320645764, 1.3067246485671786, 0.9467434115735129, 2.658520011299306, 3.3714331906571857, 2.4523023550510943, 1.966129065945084, 1.2302510407269367, 1.6404372540816863, 3.1391540001818816, 1.590788673716406, 0.537578721986061, 1.8642585438836738, 2.4556094376894073, 1.2464183750610864, 2.3403098792674735, 2.7446779307188507, 2.335806630611211, 1.4967422185971841, 1.3265846716598477, 1.4611029562125701, 1.8133247732559123, 1.231859448685497, 1.6752809081350168, 1.1806104619285496, 2.20849357777155, 3.842283277567963, 2.590361747560027, 1.4961519026660075, 1.3313922740059538, 1.5872616106165134, 1.9141926466688113, 0.9467051761268562, 1.2256459338928718, 1.313261412957074, 0.9032278885972191, 2.506022843127852, 3.217464879101322, 1.8775145904525887, 1.9850369437243374, 0.37344169875616484], 50148: [1.175044651812994, 2.282386380321226, 1.4795854911055606, 2.3161612720391935, 1.1904946929784677, 2.450897756538689, 1.8557830186926052, 2.0150546985283215, 3.0440796951424316, 2.108677032207349, 1.9043238008026637, 2.0479297836888724, 1.5763262449869149, 1.5340357555654602, 1.6911130060268431, 2.1287138475257157, 2.3197074892183482, 3.437582458931899, 2.422630341879635, 1.4793824497929082, 2.9175120428539416, 2.6121254932557942, 2.767725795775911, 2.1944292957432756, 1.903888851093967, 1.9883531748342795, 1.392115988397695, 1.0791752905912937, 1.7071198307383757, 2.3007692431630278, 2.568080995187043, 1.3593489219649875, 1.5052106417510855, 2.1817818257663846, 1.528465677444986, 1.222028636864795, 2.986252673457784, 2.9740550787591493, 1.259974609353101, 0.9290437169309842, 1.4783088532803506, 2.7855437627183215, 2.773948418670249, 1.5670713563850551, 0.6355182181194993, 1.2497163879526394, 1.8059675108705318, 1.1048366061959025, 1.939970244369011, 3.250149774080705, 2.1104028641043824, 1.3418373898451585, 1.0606408557219693, 1.431855109936181, 1.717814220767248, 1.1608812290071817, 1.4539839010391309, 1.316976211926644, 2.5636136812651062, 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1.1251405905292173, 0.9086325315046945, 2.618514774169426, 2.7978271439082283, 1.4585476654244076, 1.6530436492019258, 0.4656005822518477], 51493: [1.2296752852651072, 1.880099208866926, 0.349755608572512, 1.6436914437005783, 1.4230403583624027, 3.663199724646769, 2.896598515156748, 2.534233941370557, 3.2297482899841072, 1.8990793021292347, 1.1891891883394383, 1.5450535552210287, 1.5547591980084756, 1.0523313246860955, 1.001076666387949, 1.6570935574904369, 2.5782582361458752, 3.4941168072867375, 1.5622388811322003, 0.6151089320324677, 2.515333301217313, 1.811375483695547, 2.6628929416486966, 1.8678831681280903, 1.2650963906062782, 1.6339809759277921, 0.4668542281102485, 0.3829824520386648, 0.6223224078838544, 2.3348309177608857, 3.1363315219567127, 0.8861534210621873, 1.6125913266934324, 1.8480994166653553, 1.2005192065700734, 0.823180295727847, 2.3840250686949593, 2.7943872195587365, 1.2417845155368494, 1.4200635714760723, 1.7322946633633574, 1.823147142316733, 2.2386411062645983, 1.5759997872394282, 0.3167559868678462, 0.9985088254952419, 2.10345328521793, 0.9672882245942777, 1.7901756366700947, 2.5509576793384556, 1.6219740843170065, 1.143981383925313, 0.8970271120926736, 1.3870155391039272, 1.6212960753532133, 1.048899069649223, 1.1455984072889411, 0.7053807279367703, 2.346232914827042, 2.2143452518761984, 2.4548949487192653, 0.6841164186016231, 1.5632569624220007, 1.5979327640809782, 1.712324851827656, 1.0802719332382031, 0.7949738004023653, 1.5038636248779367, 1.1068680941583553, 3.774063584081562, 3.063384325628797, 1.383264587922592, 1.4652948149418792, 0.8439843198953777, 1.150464818726776, 1.5454672040710915, 0.30664762853823574, 1.5434283975229197, 1.4112962878576305, 2.9030920141725742, 2.794414561296307, 2.0848856672876805, 2.9792341492801793, 2.3039092883052295, 2.3198235205891216, 1.681615674771768, 2.0328607101568337, 1.0560444412786363, 1.17446822666868, 1.3671023551113801, 3.4517743769551656, 4.5106319656795835, 2.3843298907119093, 0.736151423562384, 2.63921989252968, 2.128449040772401, 2.257021978525281, 2.1500116254782613, 1.3835513620491395, 1.4615763783262585, 0.4446831818387221, 0.48615993913722333, 0.6629088629860798, 2.168275414228908, 2.583458210659278, 1.0230857585624005, 2.0027631572222226, 1.7456808043590142, 0.7755473040631868, 0.48242750548884666, 2.593007038391632, 3.0544558018172205, 2.2258000835772664, 1.8748089779897974, 1.2368040964742923, 1.5322296976186898, 1.7806299031075163, 1.7618469688869955, 0.4370930250352929, 1.4786887187377347, 2.3227608471734666, 0.803207806572008, 1.9916050157022736, 2.820716836603495, 1.2354775953039936, 0.9808865915822187, 1.1534629412068762, 1.5579653005281302, 1.14806905415407, 0.9785636651613646, 1.5165238577543145, 1.1562677996813286, 2.8895171056859565, 3.1229938615533164, 2.139002354743003, 0.9977955888095245, 0.9963820403292066, 1.723628555456278, 1.4975769214393533, 1.1133960206334779, 0.8724303462256345, 1.1740208208759142, 0.8354779197369833, 1.5598629507349548, 3.5147985176739067, 1.4096632035491612, 1.6448412438624107, 0.7949567065174968], 51339: [0.9977549495603671, 1.723037286984426, 0.7017374409971472, 1.4364471227728854, 1.142353997448677, 2.829672479727629, 2.2277653908571846, 1.7073512160111386, 2.782327549509954, 1.599107193418655, 0.8921622691070958, 1.6264709827645358, 1.4718942749623287, 1.082867181640492, 1.2320885965221078, 1.4833943679124437, 2.8648492669060315, 3.622101355950009, 1.5648037596337732, 0.987078877052881, 2.4742827528317486, 1.497073306266506, 2.484853560934723, 1.6903647987198085, 1.1809139172982113, 1.3331679989922387, 0.6518999723369336, 0.554472258442655, 1.0873682210415605, 1.7236594481402363, 2.0696049873566316, 0.6273553093276543, 1.295119851070887, 1.7474025396025301, 1.3897585405031974, 0.9192816151283586, 2.3861307342203513, 2.159768790851396, 1.4212826385004658, 1.8182697337113654, 1.1568075437482999, 1.5449941528216269, 2.706106243222285, 1.015864522817509, 0.3798444930981717, 0.9087584433523719, 1.5117084294886016, 0.9386461002130378, 1.8212741896610918, 2.899654257145822, 2.169971806636852, 1.0879761098070315, 0.5731490615679167, 0.9289218383399691, 0.8271660039147867, 0.6861950215998041, 1.4122068406321584, 0.9756324804426324, 1.9910083974503807, 2.7173349300923686, 2.229056850121352, 0.1529718959052946, 1.1206763883341115, 1.3408404202555488, 1.0994662278458684, 0.6890522938144772, 0.7223086916555977, 1.159163372787494, 0.9305113511690857, 3.050315242230534, 2.1434902269318865, 1.3059337777937052, 1.5838183164218078, 0.49743660684726937, 0.8195078469528558, 2.4216636891322376, 0.5092448131128575, 1.3567791429507263, 1.0343228522906636, 1.701259306357327, 1.7579330756224194, 1.8492866705254276, 3.114046019647928, 2.387092894704879, 1.0712230900124298, 1.717537195118631, 1.6606775649515317, 0.8423141191730374, 0.9862693091673673, 1.1334007368551824, 3.1700516705669006, 3.596610461026105, 2.3010144535927948, 0.7216246511773614, 2.4581419888817924, 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1.3004125453930726, 1.3971611692740644, 0.31992387411516526]}

output_df = pd.DataFrame.from_dict(copied_output)
output_df

	50005	50243	50023	50148	50029	50823	50057	50191	50119	50686	...	51363	51372	51373	51479	51146	51480	51483	51482	51493	51339
0	1.884312	1.808494	1.639492	1.175045	1.926571	1.341950	1.611954	1.178617	0.895068	1.137727	...	1.020349	1.014068	1.024022	1.063714	1.330252	1.997742	1.266157	1.270886	1.229675	0.997755
1	1.763203	2.758280	2.286924	2.282386	2.848637	2.395108	2.129435	2.740201	2.058616	2.620140	...	1.456329	1.849086	2.252372	1.199759	2.137025	2.194494	1.616737	1.681414	1.880099	1.723037
2	1.113907	1.378670	1.098771	1.479585	1.265451	1.318799	1.567942	0.846326	1.257798	1.100486	...	0.545434	0.805782	0.533919	0.348924	0.854489	0.875476	0.429743	0.359538	0.349756	0.701737
3	1.489480	2.164748	1.940247	2.316161	2.432516	2.397342	1.904859	1.703106	2.015547	1.604921	...	1.183528	1.623594	1.272603	1.223189	2.138176	2.139823	2.086772	1.628488	1.643691	1.436447
4	1.574176	2.009253	1.220210	1.190495	2.827137	1.364059	2.328978	1.214955	1.188583	1.166113	...	0.734854	1.293781	0.946754	0.963564	1.258846	1.769235	1.293948	1.316159	1.423040	1.142354
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
143	2.634995	2.692000	2.506023	2.163953	2.934882	2.226297	1.893980	2.551567	2.020542	2.408232	...	1.913473	2.066965	1.673923	2.653675	1.899904	2.225462	2.665970	2.618515	1.559863	1.626178
144	2.486487	3.459162	3.217465	2.471602	3.365702	3.436234	2.497402	3.629664	2.931455	2.951812	...	2.244897	2.808212	2.490250	2.494002	2.575238	2.850502	2.761292	2.797827	3.514799	2.739708
145	1.813947	2.029375	1.877515	1.459971	2.055749	2.392670	1.889100	2.573372	2.007722	1.695569	...	1.343610	1.861284	1.524503	2.089433	1.695396	1.525701	1.510985	1.458548	1.409663	1.300413
146	1.377991	1.806214	1.985037	1.644803	1.884782	1.806623	1.723144	1.787945	1.731221	1.768279	...	1.238045	1.627495	1.404834	1.365170	1.657661	1.613756	1.240207	1.653044	1.644841	1.397161
147	0.450372	0.560012	0.373442	0.404269	0.749440	0.814612	0.764688	0.534441	0.695938	0.637771	...	0.245704	0.585838	0.285328	0.352315	0.402193	0.486928	0.337399	0.465601	0.794957	0.319924

148 rows × 311 columns

We can see here that the new dataframe we have created has the participant IDs in the columns and the items in the rows. We need to change this to create the correlation heat map. To do this we will transpose the dataframe.

transposed_output = output_df.T

transposed_df = pd.DataFrame(transposed_output)
transposed_df

	0	1	2	3	4	5	6	7	8	9	...	138	139	140	141	142	143	144	145	146	147
50005	1.884312	1.763203	1.113907	1.489480	1.574176	2.979951	2.382822	2.251274	3.483743	1.819774	...	1.268674	1.332451	1.092183	1.129284	1.001832	2.634995	2.486487	1.813947	1.377991	0.450372
50243	1.808494	2.758280	1.378670	2.164748	2.009253	3.399129	2.550344	2.685849	3.455819	2.275358	...	1.601060	1.471437	1.004129	1.408076	1.310058	2.692000	3.459162	2.029375	1.806214	0.560012
50023	1.639492	2.286924	1.098771	1.940247	1.220210	3.271414	2.556528	2.346739	3.034480	1.573858	...	1.914193	0.946705	1.225646	1.313261	0.903228	2.506023	3.217465	1.877515	1.985037	0.373442
50148	1.175045	2.282386	1.479585	2.316161	1.190495	2.450898	1.855783	2.015055	3.044080	2.108677	...	1.920194	1.212740	1.049676	1.063514	1.120005	2.163953	2.471602	1.459971	1.644803	0.404269
50029	1.926571	2.848637	1.265451	2.432516	2.827137	3.119827	2.673950	2.967056	3.506666	2.363364	...	1.887636	1.113874	1.804394	1.438875	1.418120	2.934882	3.365702	2.055749	1.884782	0.749440
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
51480	1.997742	2.194494	0.875476	2.139823	1.769235	2.945434	2.816536	2.009279	2.951361	1.508404	...	1.781824	1.270997	1.221007	1.174207	1.034510	2.225462	2.850502	1.525701	1.613756	0.486928
51483	1.266157	1.616737	0.429743	2.086772	1.293948	3.121307	2.470730	1.940298	3.198552	1.873882	...	1.268609	0.999915	0.918082	1.451173	0.875697	2.665970	2.761292	1.510985	1.240207	0.337399
51482	1.270886	1.681414	0.359538	1.628488	1.316159	3.544188	2.184196	2.038821	2.607549	1.187186	...	1.557960	1.161430	0.806849	1.125141	0.908633	2.618515	2.797827	1.458548	1.653044	0.465601
51493	1.229675	1.880099	0.349756	1.643691	1.423040	3.663200	2.896599	2.534234	3.229748	1.899079	...	1.497577	1.113396	0.872430	1.174021	0.835478	1.559863	3.514799	1.409663	1.644841	0.794957
51339	0.997755	1.723037	0.701737	1.436447	1.142354	2.829672	2.227765	1.707351	2.782328	1.599107	...	0.949336	0.884236	0.813673	0.859555	0.534621	1.626178	2.739708	1.300413	1.397161	0.319924

311 rows × 148 columns

plt.figure(figsize=(10,7))
sns.heatmap(transposed_df.corr(), xticklabels=False, cmap='rocket')

<AxesSubplot:>

../_images/17d81287ac922e46a9669d63682e914bc55c83e4deb14a1efb87cb5e78b61f54.png

Here we have our correlation heat map of the 148 different average cortical thickness scores. We can see that the correlations are relatively high between the different brain areas.

4.2. Logistic Regression#

Now that we have gotten a good look at what the relationship between our data points looks like, we can carry out a logistic regression. The logistic regression aims to solve classification problems, by prediction categories in our data points as opposed to prediction one continuous outcome. Since we already know the categories we are working with, which is presented in the simplest case of binomial categories, we can train our logistic regression model to predict our categories and then assess how well this was done.

To start of, we need to make sure we have all the suitable packages downloaded.

from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
from sklearn.pipeline import make_pipeline
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

successful = Data_overview.loc[Data_overview['subject'].isin(successful_download)]
successful

	Unnamed: 0	Unnamed: 0.1	SUB_ID	X	subject	SITE_ID	FILE_ID	DX_GROUP	DSM_IV_TR	AGE_AT_SCAN	...	qc_notes_rater_1	qc_anat_rater_2	qc_anat_notes_rater_2	qc_func_rater_2	qc_func_notes_rater_2	qc_anat_rater_3	qc_anat_notes_rater_3	qc_func_rater_3	qc_func_notes_rater_3	SUB_IN_SMP
1	1	2	50003	2	50003	PITT	Pitt_0050003	1	1	24.45	...	NaN	OK	NaN	OK	NaN	OK	NaN	OK	NaN	1
3	3	4	50005	4	50005	PITT	Pitt_0050005	1	1	13.73	...	NaN	OK	NaN	maybe	ic-parietal-cerebellum	OK	NaN	OK	NaN	0
19	19	20	50023	20	50023	PITT	Pitt_0050023	1	1	12.64	...	NaN	OK	NaN	maybe	ic-cerebellum_temporal_lobe	OK	NaN	OK	NaN	0
25	25	26	50029	26	50029	PITT	Pitt_0050029	1	1	11.40	...	NaN	maybe	skull-striping fail	fail	ic-parietal-cerebellum	OK	NaN	OK	NaN	0
32	32	33	50036	33	50036	PITT	Pitt_0050036	2	0	13.28	...	NaN	OK	NaN	maybe	ic-cerebellum	OK	NaN	OK	NaN	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1079	1079	1080	51493	1080	51493	CALTECH	Caltech_0051493	2	0	29.20	...	NaN	OK	NaN	maybe	Ic-parietal-minor	OK	NaN	OK	NaN	0
1080	1080	1081	51556	1081	51556	SBL	SBL_0051556	2	0	20.00	...	NaN	OK	NaN	maybe	ic-cerebellum-temporal_lobe	OK	NaN	OK	NaN	0
1083	1083	1084	51559	1084	51559	SBL	SBL_0051559	2	0	27.00	...	NaN	OK	NaN	maybe	ic-cerebellum	OK	NaN	OK	NaN	0
1106	1106	1107	51582	1107	51582	SBL	SBL_0051582	1	2	31.00	...	NaN	OK	NaN	maybe	ic-cerebellum	OK	NaN	OK	NaN	0
1110	1110	1111	51606	1111	51606	MAX_MUN	MaxMun_a_0051606	1	2	29.00	...	NaN	OK	NaN	maybe	ic-cerebellum	OK	NaN	OK	NaN	0

311 rows × 106 columns

We must now define what date our X and our y groups will entail. The X group will consist of our average cortical thickness data - or our features. And our y group will hold our sample.

y = successful[['DX_GROUP', 'SEX']].to_numpy()

X = np.zeros((len(successful), 148))
y = np.zeros((len(successful), 2))


for idx, subject in enumerate(successful['subject'].to_numpy()):
    try:
        X[idx] = np.load('/Users/gretivan/Desktop/Neuro_data/sub-00%s_destrieux-corticalthickness.npy'%(subject))
        y[idx] = successful.loc[successful['subject'] == subject][['DX_GROUP', 'SEX']].to_numpy()
    except FileNotFoundError:
        print(subject)
            

Now that we have defined X and y, we can set up our model for the logistic regression.

from sklearn.model_selection import train_test_split

scalar = preprocessing.StandardScaler()

y_pred = np.zeros(len(y)) 

skf = StratifiedKFold(n_splits=2)
skf.get_n_splits(X, y)

print(skf)
StratifiedKFold(n_splits=2, random_state=0, shuffle=True)

StratifiedKFold(n_splits=2, random_state=None, shuffle=False)

StratifiedKFold(n_splits=2, random_state=0, shuffle=True)

Stratified K fold cross validation was used with 2 splits due to the size of the dataset. This cross-validation specific cross-validation returns folds that are made by preserving the percentage of samples for each class. The consideration of using repeated K-fold cross-validation was rejected as our data is already split into groups. Thus stratifying it by group is more valuable than using random splits.

While setting up the model, we decided to use the model as our pipeline which was previously discussed. This means that the model can be adapted and changed for a variety of analyses. The first model analysis we are doing is a logistic regression as mentioned above.

from sklearn.linear_model import LogisticRegression
def custom_pipeline(X, y, group):
    for i, (train_index, test_index) in enumerate(skf.split(X, y[:,group])):
        x_train = X[train_index]
        x_test = X[test_index]
        y_train = y[train_index, 0]
        y_test = y[test_index, 0]

        x_train = scalar.fit_transform(x_train)

        clf = LogisticRegression(C=0.1, random_state=0)
        clf.fit(x_train, y_train)
        y_pred[test_index] = clf.predict(scalar.transform(x_test))
        score_test = clf.score(scalar.transform(x_test),y_test)
        score_train = clf.score(x_train,y_train)
    
    return y_pred, y_train, y_test, x_train, x_test, score_test, score_train

x_transform_test = scalar.transform(x_test)

Side Note: in addition to doing a logistic regression, this model can be quickly and easily adapted to do other analyses such as Support vector machines (SVMs) which are a set of supervised learning methods used for classification, regression and outliers detection.

Since we used a normalisation for our x_test and our x_train data sets, we can now plot them to see whether they look similar, and normal, in comparison to X.

sns.histplot(x_train.ravel())
sns.histplot(x_transform_test.ravel(), color='red')
sns.histplot(X.ravel(), color='green')

plt.xlabel('')
plt.title('Normalisation comparison')
plt.legend(labels=['x_train', 'x_test', 'X'])

plt.show()

../_images/643fa04cbc8995cc695785dd1f93ed203c37bb7d341ed8eb14facf10fe6e5bdd.png

As we can see here, when compared to the original, non normalised dataset we can see a large difference. This is good, as it means that we are now working with normalised data in the x_train and x_test datasets.

Now that we have defined our model, we can apply it to our two features of interest - sex and group.

y_asd,y_train_asd,y_test_asd, x_train_asd, x_test_asd,score_test_asd,score_train_asd = model_func(X,y,group=0)

As previously defined, group 0 is the group, meaning ASD vs. control, and group 1 is sex, meaning male vs. female, which was previously defined.

y_sex,y_train_sex,y_test_sex, x_train_sex, x_test_sex, score_test_sex, score_train_sex = model_func(X,y,group=1)

With the now defined models, one for group and one for sex, we can check out the scores.

score_test_asd

0.5806451612903226

score_test_sex

0.5290322580645161

Both our group score and our sex score are relatively similar, which suggests that our prediction is equally good for both the group and sex. Our chance accuracy was only slightly above 50% which suggests that our model is subsstandard. The reasons for this will be discussed later on. We can compare these predictions to our training set on which our model is based.

score_train_asd

0.9230769230769231

score_train_sex

0.9166666666666666

The prediction scores for the training sets are, as expected relatively high, since our model is based on the training set. The difference between our train and our tests sets, that being a high score on the train set and a low score on the test set seems to indicate poor generalisation. Thus we are speaking of an overfitting model.

4.3. Visualisation and Classification#

As with every other analysis, plotting these predictions allows for better visualisation of the results. For easier visibility, we decided to create one plot containing the predictions for group and sex, as well as a prediction for the whole test set compared to y.

from sklearn.metrics import confusion_matrix
import seaborn as sns
import matplotlib.pyplot as plt

cm_group = confusion_matrix(y_test_asd, y_asd[155:310])
cm_sex = confusion_matrix(y_test_sex, y_sex[155:310])

figure, axes = plt.subplots(1, 2, figsize=(17,7))

sns.heatmap(cm_group, annot = True, ax=axes[0]).set(title='Heatmap for Group predictions', ylabel='True Score', xlabel='Predicted Score');
sns.heatmap(cm_sex, annot = True, ax=axes[1]).set(title='Heatmap for sex predictions', ylabel='True Score', xlabel='Predicted Score');

figure.suptitle('Confusion Matrix for predictions');

../_images/54ee51805f8c073933dc71f90b904847735a9ab477b54b9daadafd8b1030cae5.png

Note:

for the first heatmap: 0 = asd, 1 = control
for the second heatmap: 0 = male, 1 = female

To assess the accuracy of these heatmaps, looking into true positives and false negatives, we can use two methods: the precision_recall_fscore_support package, or the classification report package from sklearn. Both of these packages show the same thing in a different lay out. Lets first look at their layouts and decide which to use.

import sklearn
from sklearn.metrics import precision_recall_fscore_support
from sklearn.metrics import classification_report

sklearn.metrics.precision_recall_fscore_support(y_test_asd, y_asd[155:310])

(array([0.42222222, 0.62727273]),
 array([0.31666667, 0.72631579]),
 array([0.36190476, 0.67317073]),
 array([60, 95]))

We can see here that the output is relatively confusing to look at. Lets look at the classification report.

print(classification_report(y_test_asd, y_asd[155:310]))

              precision    recall  f1-score   support

         1.0       0.46      0.30      0.36        60
         2.0       0.64      0.78      0.70        95

    accuracy                           0.59       155
   macro avg       0.55      0.54      0.53       155
weighted avg       0.57      0.59      0.57       155

This is much better. We can see by comparing the two that the scores for each (precision, recall, f1-score and support) are the same for 1.0 and 2.0, thus we shall continue this way.

This classification report shows:

Precision: Out of all the participants that the model predicted would get have ASD, only 46% actually did.
Recall: Out of all the participants that actually did have ASD, the model only predicted this outcome correctly for 30% of those participants

Since our f-score is only 0.36, we can assume that our model does not do a great job of predicting whether someone has ASD or is a control participant.

However, since our precision, recall and f1-score for our control participants is 64%, 78% and 0.70 respectively, it suggests that our model is good at predicting which brain is the brain of a control participant.

print(classification_report(y_test_sex, y_sex[155:310]))

              precision    recall  f1-score   support

         1.0       0.46      0.32      0.37        57
         2.0       0.66      0.79      0.72        98

    accuracy                           0.61       155
   macro avg       0.56      0.55      0.55       155
weighted avg       0.59      0.61      0.59       155

Precision: Out of all the participants that the model predicted would be male, only 46% actually were.
Recall: Out of all the participants that actually were male, the model only predicted this outcome correctly for 32% of those participants

Our f-score for sex prediction is 0.37 which is similar to the prediction of asd.

In line with the results provided by the model for group prediction, the model was also much better at predicting the female sex with precision, recall and f1-scores being much higher - 66% were actually female, 79% were correctly predicted, and 0.72 as the f-score.

Thus we can conclude that our model is at best subpar at predicting the male sex and whether someone has ASD.

4.4. Grid Search#

Because of this reason, we might want to use a grid search. This is a tuning technique that attempts to compute the optimum values of hyperparameters.

from sklearn.model_selection import GridSearchCV

param_grid = {'penalty': ['l1', 'l2'], 'C':np.logspace(-4, 4, 20), 'solver': ['liblinear']}
print(param_grid)

{'penalty': ['l1', 'l2'], 'C': array([1.00000000e-04, 2.63665090e-04, 6.95192796e-04, 1.83298071e-03,
       4.83293024e-03, 1.27427499e-02, 3.35981829e-02, 8.85866790e-02,
       2.33572147e-01, 6.15848211e-01, 1.62377674e+00, 4.28133240e+00,
       1.12883789e+01, 2.97635144e+01, 7.84759970e+01, 2.06913808e+02,
       5.45559478e+02, 1.43844989e+03, 3.79269019e+03, 1.00000000e+04]), 'solver': ['liblinear']}

grid=GridSearchCV(LogisticRegression(), param_grid, cv=5, refit=True)
group_grid = grid.fit(x_train_asd, y_train_asd)
group_grid

GridSearchCV(cv=5, estimator=LogisticRegression(),
             param_grid={'C': array([1.00000000e-04, 2.63665090e-04, 6.95192796e-04, 1.83298071e-03,
       4.83293024e-03, 1.27427499e-02, 3.35981829e-02, 8.85866790e-02,
       2.33572147e-01, 6.15848211e-01, 1.62377674e+00, 4.28133240e+00,
       1.12883789e+01, 2.97635144e+01, 7.84759970e+01, 2.06913808e+02,
       5.45559478e+02, 1.43844989e+03, 3.79269019e+03, 1.00000000e+04]),
                         'penalty': ['l1', 'l2'], 'solver': ['liblinear']})

sex_grid = grid.fit(x_train_sex, y_train_sex)
sex_grid

GridSearchCV(cv=5, estimator=LogisticRegression(),
             param_grid={'C': array([1.00000000e-04, 2.63665090e-04, 6.95192796e-04, 1.83298071e-03,
       4.83293024e-03, 1.27427499e-02, 3.35981829e-02, 8.85866790e-02,
       2.33572147e-01, 6.15848211e-01, 1.62377674e+00, 4.28133240e+00,
       1.12883789e+01, 2.97635144e+01, 7.84759970e+01, 2.06913808e+02,
       5.45559478e+02, 1.43844989e+03, 3.79269019e+03, 1.00000000e+04]),
                         'penalty': ['l1', 'l2'], 'solver': ['liblinear']})

grid.best_params_

{'C': 0.23357214690901212, 'penalty': 'l2', 'solver': 'liblinear'}

grid_asd_test = grid.predict(x_test_asd)
grid_sex_test = grid.predict(x_test_sex)

To check our grid search, we can again use a classification report and compare this with the classificationr report above.

print(classification_report(y_test_asd, grid_asd_test))

              precision    recall  f1-score   support

         1.0       0.37      0.78      0.50        60
         2.0       0.52      0.15      0.23        95

    accuracy                           0.39       155
   macro avg       0.44      0.47      0.36       155
weighted avg       0.46      0.39      0.33       155

Parameter tuning did not make the model for group predictions any better, rather it made worse. Thus we can conclude here that we tried tuning hyper parameters but it did not make a difference.

print(classification_report(y_test_sex, grid_sex_test))

              precision    recall  f1-score   support

         1.0       0.40      0.56      0.46        57
         2.0       0.66      0.50      0.57        98

    accuracy                           0.52       155
   macro avg       0.53      0.53      0.52       155
weighted avg       0.56      0.52      0.53       155

Surprisingly, hypter tuning did make a difference for sex. It seemed to have even out the scores a little bit, that now the predictions for male and female are roughly even. Though the model is still not great.

4.5. Feature Selection#

Another consideration to improve the model could be using a feature selection method called SelectKBest. This method picks out the best features according to k scores.

from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import f_classif

In order to look into this we need to define a new variable X which contains a certain number of k selects. We will choose 3 different k selects to see what happes. Please see future directions.

X_15 = SelectKBest(f_classif, k=10).fit_transform(X, y[:,0])
X_80 = SelectKBest(f_classif, k=80).fit_transform(X, y[:,0])
X_145 = SelectKBest(f_classif, k=145).fit_transform(X, y[:,0])

20 K-selects#

y_group_15,y_train_group_15,y_test_group_15, x_train_group_15, x_test_group_15, score_test_group_15, score_train_group_15 = model_func(X_15,y,group=0)

y_sex_15,y_train_sex_15,y_test_sex_15, x_train_sex_15, x_test_sex_15, score_test_sex_15, score_train_sex_15 = model_func(X_15,y,group=1)

score_test_group_15, score_train_group_15

(0.6645161290322581, 0.6410256410256411)

score_test_sex_new, score_train_sex_new

(0.632258064516129, 0.717948717948718)

80 K-selects#

y_group_80,y_train_group_80,y_test_group_80, x_train_group_80, x_test_group_80, score_test_group_80, score_train_group_80 = model_func(X_80,y,group=0)

y_sex_80,y_train_sex_80,y_test_sex_80, x_train_sex_80, x_test_sex_80, score_test_sex_80, score_train_sex_80 = model_func(X_80,y,group=1)

score_test_group_80, score_train_group_80

(0.5741935483870968, 0.8269230769230769)

score_test_sex_80, score_train_sex_80

(0.5483870967741935, 0.8012820512820513)

145 K-Selects#

y_group_145,y_train_group_145,y_test_group_145, x_train_group_145, x_test_group_145, score_test_group_145, score_train_group_145 = model_func(X_145,y,group=0)

y_sex_145,y_train_sex_145,y_test_sex_145, x_train_sex_145, x_test_sex_145, score_test_sex_145, score_train_sex_145 = model_func(X_145,y,group=1)

score_test_group_145, score_train_group_145

(0.5741935483870968, 0.9230769230769231)

score_test_sex_145, score_train_sex_145

(0.535483870967742, 0.9102564102564102)

Ideally, you would want both train and test scores to be high. From looking at the comparisons between 15, 80 and 145 features, it appears that maybe less features are better for the model as the more features get added, the higher the train score becomes but the test score, which is the score of importance gets lower. For 15 selects both train and test scores are even, yet still relatively low. Since picking out the features changes the test and train scores, there must be something going on with the features.

5. Future Directions#

There are various future directions that we suggest for anyone wishing to repeat this project.

The first aspect which might benefit changing, if this research were replicated, could be using the newer ABIDE II dataset. This dataset provides an even larger aggregation of datasets with a wider phenoypic variation, as well as a characterisation of core and associated ASD symptoms. On top of this, 2 research groups even provide longitudinal samples from 38 individuals. A more comprehensive sample with more clear cut phenotypic differences may provide better results. These two datasets could even be combined to create a very large sample.
It might be useful for future researchers to test how different atlases to mask the data in different ways to test how this will later affect analysis. Some other Atlases that we recommend are The Whole Brain Atlas from Harvard medical school and the Desikan-Killiany Atlas. Doing this will partialise out a different set of brain areas which might provide more influential and clear results.
In terms of analysis, it would be recommended to assess the effect of certain feature selections in more detail, for example by looping over feature selection within the model to see how this changes the accuracy of the logistic regression. As we saw in our results regarding feature selection, the features do seem to play an important role in our regression scores.
A final and interesting further direction, which was also mentioned which building the model, is the use of SVMs. For this the model simply has to include a different method - the logistic regression is interchangeable. The SVM would be interesting as it can be used to predict discrete labels for each sample.

1. Data Overview

Contents

1. Data Overview#

1.1. Loading the data#

1.2. Assessing the demographics#

1.3. Matching#

1.4. Visualizing#

1.5. Demographic information of matched sample#

2. Practice Exploring the data#

2.1. Practice Download#

2.2. Visualizing#

2.3. Deciding on and loading the Atlas#

2.4. Checking the atlas space#

2.5. Adjusting the image#

2.6. Testing the masker#

3. Exploring the full dataset#

3.1. Downloading the matched dataset#

3.2. Rechecking the demographic information#

3.3. Apply the transformations#

4. Data Analysis#

4.1. Correlation#

4.2. Logistic Regression#

4.3. Visualisation and Classification#

4.4. Grid Search#

4.5. Feature Selection#

20 K-selects#

80 K-selects#

145 K-Selects#

5. Future Directions#